This paper shows that the problem of web services representation is crucial and analyzes the various factors that influence on it. It presents the traditional representation of web services considering traditional textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services descriptions are dirty and need significant cleaning to keep only useful information. To deal with this problem, we introduce rules based text tagging method, which allows filtering web service description to keep only significant information. A new representation based on such filtered data is then introduced. Many web services have empty descriptions. Also, we consider web services representations based on the
WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called symbolic reputation, which is computed from relationships between web services. The impact of the use of these representations on web service discovery and recommendation is studied and discussed in the
experimentation using real world web services.
Web Services Discovery and Recommendation Based on Information Extraction and...ijwscjournal
This paper shows that the problem of web services representation is crucial and analyzes the various
factors that influence on it. It presents the traditional representation of web services considering traditional
textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services
descriptions are dirty and need significant cleaning to keep only useful information. To deal with this
problem, we introduce rules based text tagging method, which allows filtering web service description to
keep only significant information. A new representation based on such filtered data is then introduced.
Many web services have empty descriptions. Also, we consider web services representations based on the
WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called
symbolic reputation, which is computed from relationships between web services. The impact of the use of
these representations on web service discovery and recommendation is studied and discussed in the
experimentation using real world web services.
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable
form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
AN ARCHITECTURE FOR WEB SERVICE SIMILARITY EVALUATION BASED ON THEIR FUNCTION...ijwscjournal
By increasing popularity of SOC, using Web services in applications has increased too. SOC creates a loosely coupled environment in which the actual execution environment might differ significantly from the one with the presupposed conditions during application design. Therefore, although an appropriate Web service might have been selected, by passing time, the Web service may not be efficient enough or may not be applicable under specific conditions.
For service-oriented systems to be flexible and self-adaptive, it is necessary to automatically select and use a similar service instead of the one which causes the above mentioned problems. Finding a similar service means specifying the proper services which fulfill the same requirements as those fulfilled by the problematic service.
In most of the previous works, a number of the best services (k) are selected and ordered based on functional similarity. The user must select one of these services based on his/her preferences. One important metric in selecting a similar service is considering QoS properties and user preferences about QoS. Because of the importance of this issue, in the present paper, an architecture is proposed in which, in addition to functional similarity, QoS properties and user preferences are also considered in selecting a similar service.
Web service discovery methods and techniques a reviewijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are modular, self-describing, self-contained and loosely coupled applications that can be
published, located, and invoked across the web. With the increasing number of web services available on
the web, the need for web services composition is becoming more and more important. Nowadays, for
answering complex needs of users, the construction of new web services based on existing ones is required.
This problem is known as web services composition. However, it is one of big challenge problems of recent
years in a distributed and dynamic environment. The various approaches in field of web service
compositions proposed by the researchers. In this paper we present a review of existing approaches for
web service composition and compare them among each other with respect to some key requirements. We
hope this paper helps researchers to focus on their efforts and to deliver lasting solutions in this field.
WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEWijcseit
This document provides a review of existing approaches for web service composition. It begins with an introduction and background on key concepts related to web services and composition such as ontologies, semantic web architecture, semantic annotations, reasoners, matching, and quality of service. It then discusses current methods for web service composition, distinguishing between manual/static composition done at design time versus automatic/dynamic composition done at runtime. The document categorizes and compares different composition approaches and aims to help focus future research efforts.
Semantic web services discovery selection and composition techniquescsandit
Web services are already one of the most important resources on the Internet. As an integrated
solution for realizing the vision of the Next Generation Web, semantic web services combine
semantic web technology with web service technology, envisioning automated life cycle
management of web services. This paper discusses the significance and importance of service
discovery & selection to business logic, and the requisite current research in the various phases
of the semantic web service lifecycle like discovery and selection. We also present several
different composition strategies, based on current research, and provide an outlook towards
critical future work.
SEMANTIC WEB SERVICES – DISCOVERY, SELECTION AND COMPOSITION TECHNIQUEScscpconf
Web services are already one of the most important resources on the Internet. As an integrated solution for realizing the vision of the Next Generation Web, semantic web services combine semantic web technology with web service technology, envisioning automated life cycle
management of web services. This paper discusses the significance and importance of service
discovery & selection to business logic, and the requisite current research in the various phases of the semantic web service lifecycle like discovery and selection. We also present several different composition strategies, based on current research, and provide an outlook towards critical future work
Web Services Discovery and Recommendation Based on Information Extraction and...ijwscjournal
This paper shows that the problem of web services representation is crucial and analyzes the various
factors that influence on it. It presents the traditional representation of web services considering traditional
textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services
descriptions are dirty and need significant cleaning to keep only useful information. To deal with this
problem, we introduce rules based text tagging method, which allows filtering web service description to
keep only significant information. A new representation based on such filtered data is then introduced.
Many web services have empty descriptions. Also, we consider web services representations based on the
WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called
symbolic reputation, which is computed from relationships between web services. The impact of the use of
these representations on web service discovery and recommendation is studied and discussed in the
experimentation using real world web services.
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable
form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
AN ARCHITECTURE FOR WEB SERVICE SIMILARITY EVALUATION BASED ON THEIR FUNCTION...ijwscjournal
By increasing popularity of SOC, using Web services in applications has increased too. SOC creates a loosely coupled environment in which the actual execution environment might differ significantly from the one with the presupposed conditions during application design. Therefore, although an appropriate Web service might have been selected, by passing time, the Web service may not be efficient enough or may not be applicable under specific conditions.
For service-oriented systems to be flexible and self-adaptive, it is necessary to automatically select and use a similar service instead of the one which causes the above mentioned problems. Finding a similar service means specifying the proper services which fulfill the same requirements as those fulfilled by the problematic service.
In most of the previous works, a number of the best services (k) are selected and ordered based on functional similarity. The user must select one of these services based on his/her preferences. One important metric in selecting a similar service is considering QoS properties and user preferences about QoS. Because of the importance of this issue, in the present paper, an architecture is proposed in which, in addition to functional similarity, QoS properties and user preferences are also considered in selecting a similar service.
Web service discovery methods and techniques a reviewijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are modular, self-describing, self-contained and loosely coupled applications that can be
published, located, and invoked across the web. With the increasing number of web services available on
the web, the need for web services composition is becoming more and more important. Nowadays, for
answering complex needs of users, the construction of new web services based on existing ones is required.
This problem is known as web services composition. However, it is one of big challenge problems of recent
years in a distributed and dynamic environment. The various approaches in field of web service
compositions proposed by the researchers. In this paper we present a review of existing approaches for
web service composition and compare them among each other with respect to some key requirements. We
hope this paper helps researchers to focus on their efforts and to deliver lasting solutions in this field.
WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEWijcseit
This document provides a review of existing approaches for web service composition. It begins with an introduction and background on key concepts related to web services and composition such as ontologies, semantic web architecture, semantic annotations, reasoners, matching, and quality of service. It then discusses current methods for web service composition, distinguishing between manual/static composition done at design time versus automatic/dynamic composition done at runtime. The document categorizes and compares different composition approaches and aims to help focus future research efforts.
Semantic web services discovery selection and composition techniquescsandit
Web services are already one of the most important resources on the Internet. As an integrated
solution for realizing the vision of the Next Generation Web, semantic web services combine
semantic web technology with web service technology, envisioning automated life cycle
management of web services. This paper discusses the significance and importance of service
discovery & selection to business logic, and the requisite current research in the various phases
of the semantic web service lifecycle like discovery and selection. We also present several
different composition strategies, based on current research, and provide an outlook towards
critical future work.
SEMANTIC WEB SERVICES – DISCOVERY, SELECTION AND COMPOSITION TECHNIQUEScscpconf
Web services are already one of the most important resources on the Internet. As an integrated solution for realizing the vision of the Next Generation Web, semantic web services combine semantic web technology with web service technology, envisioning automated life cycle
management of web services. This paper discusses the significance and importance of service
discovery & selection to business logic, and the requisite current research in the various phases of the semantic web service lifecycle like discovery and selection. We also present several different composition strategies, based on current research, and provide an outlook towards critical future work
Efficient retrieval of web services using prioritization and clusteringAlexander Decker
This document discusses efficient retrieval of web services using prioritization and clustering. It proposes determining dominance relationships among service advertisements that considers multiple parameter degree of match (PDM) criteria. It introduces methods for prioritizing and clustering web services based on similarity measures and efficient algorithms like Transaction Knowledge Data Discovery (TKDD). The implementation retrieves web service description files, captures input/output parameters, compares them using similarity measures like Cosine and Jaccard, computes dominance scores, and prioritizes and clusters the retrieved web services.
IRJET- A Parameter Based Web Service Discovery with Underlying Semantics ProfileIRJET Journal
1. The document discusses various approaches for web service discovery. It describes syntax-based and semantic-based approaches.
2. It outlines several specific approaches for web service discovery including one based on schema matching that uses semantic annotations to enhance discovery. Another approach uses a neural network to calculate semantic similarities between schemas to improve matching.
3. A third approach discusses goal-oriented discovery of resources in RESTful architectures using three layers of abstraction from the agent level to service level to content level.
4. Finally, it introduces the concept of linked services for the web of data by publishing service annotations as linked data and creating services that process and generate linked data.
AGENTS AND OWL-S BASED SEMANTIC WEB SERVICE DISCOVERY WITH USER PREFERENCE SU...IJwest
Service-oriented computing (SOC) is an interdisciplinary paradigm that revolutionizes the very fabric of
distributed software development applications that adopt service-oriented architectures (SOA) can evolve
during their lifespan and adapt to changing or unpredictable environments more easily. SOA is built
around the concept of Web Services. Although the Web services constitute a revolution in Word Wide Web,
they are always regarded as non-autonomous entities and can be exploited only after their discovery. With
the help of software agents, Web services are becoming more efficient and more dynamic.
The topic of this paper is the development of an agent based approach for Web services discovery and
selection in witch, OWL-S is used to describe Web services, QoS and service customer request. We develop
an efficient semantic service matching which takes into account concepts properties to match concepts in
Web service and service customer request descriptions. Our approach is based on an architecture
composed of four layers: Web service and Request description layer, Functional match layer, QoS
computing layer and Reputation computing layer.
Survey on Semantic Web Services and its Composition AlgorithmEditor IJMTER
Service Oriented Architecture (SOA) is a collection of services. The
correspondence of these services takes place with one another. In SOA, Web Services are the
most important & promising part. Web services, adopted by Service Oriented Architecture
(SOA), are loosely coupled reusable software components that semantically encapsulate
discrete functionality and are distributed and programmatically accessible over the internet.
Web Service Composition plays an important role in SOA. Web Service Composition may be
dynamic or static. A composition process requires an algorithm to perform composition task.
Now-a-days various approaches for composition algorithm are used as required by research
task. In this study, we have done a survey on various web service composition algorithms.
The document proposes integrating the IRS-II (Internet Reasoning Service) semantic web services framework with the WSPAB (Web Service Personal Address Book) approach. This would solve limitations of each approach. WSPAB allows classification and selection of services but lacks semantic capabilities. IRS-II allows semantic description and execution of services but lacks user-specific information. The integration would leverage IRS-II's semantic abilities with WSPAB's user profiles to provide personalized, semantically enriched service discovery and selection. The document outlines the components and functioning of each approach, and how their integration could provide users with semantic, personalized web services matching their needs.
This document summarizes an approach to improve personalized ranking using associated edge weights. The key points are:
1) Personalized ranking aims to improve traditional search and retrieval by tailoring results to a user's interests. Authority flow approaches like PageRank and ObjectRank can be used for personalized ranking on entity-relationship graphs.
2) Edge-based personalization assigns different weights to edge types based on the user, affecting authority flow. The paper focuses on improving edge-based personalization by hybridizing the ScaleRank algorithm with k-means clustering.
3) ScaleRank approximates the DataApprox algorithm for ranking. The hybrid approach uses k-means clustering on ScaleRank output to further group related nodes,
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
TOWARDS UNIVERSAL RATING OF ONLINE MULTIMEDIA CONTENTcsandit
Most website classification systems have dealt with the question of classifying websites based on
their content, design, usability, layout and such, few have considered website classification
based on users’ experience. The growth of online marketing and advertisement has lead to
fierce competition that has resulted in some websites using disguise ways so as to attract users.
This may result in cases where a user visits a website and does not get the promised results. The
results are a waste of time, energy and sometimes even money for users. In this context, we design
an experiment that uses fuzzy linguistic model and data mining techniques to capture users’
experiences, we then use the k-means clustering algorithm to cluster websites based on a set of
feature vectors from the users’ perspective. The content unity is defined as the distance between
the real content and its keywords. We demonstrate the use of bisecting k-means algorithm for
this task and demonstrate that the method can incrementally learn from user’s profile on their
experience with these websites.
Web Services Supply Chains: A Literature Reviewijwscjournal
The globalization era has bought almost all services to the internet environment with multiple providers providing the same service in different ways. The same functionality is being offered
by multiple providers so it becomes necessary to differentiate one self from the crowd in order to thrive against the cut throat competition from the service provider’s view. On the other hand it is absolutely essential for the client to select a service provider who meets not only the functional requirements of the client but also provides the best possible quality of service (QoS) to the
customers. The services industry has contributed approximately 55.3% to the GDP of India in the year 2009. The service sector employs about 34% of the labor force in India [CIA report]. The
exponentially growing service industry has started making its global presence through the internet enabled online environment. The web environment has made almost all kind of services available online be it a simple movie ticket booking or the most complex processes such as outsourcing, funds transfer and others. The online services are special kind of services as compared to the static or product supply chain which is more offline or the semi online service supply chains in
which part of the service is offline and partly online.
Cluster based approach for Service Discovery using Pattern RecognitionYogesh Santhan
Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
A review on framework and quality of service based web services discoveryMustafa Algaet
In consequence these services are nowadays accessible to the final clients. In the last few years, more
and more Web Services providing the same functionalities are available in the environment. In order to
select the best service adapted to client’s requests, we need some method capable to evaluate and
compare different services providing the same functionalities. In this context, Quality of service can be defined as the capability to respond to the requirements (constraints) of a client and to fulfill these
needs with the best criteria (preferences) established by the client. It is calculated based on the non-
functional properties of the service. This paper provides an overview of a research progress in Quality
of Service Based Web Services Discovery; it also highlights the issues that need to be investigated in
Quality of Service Based Web Services
Project - UG - BTech IT - Cluster based Approach for Service Discovery using ...Yogesh Santhan
Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
This is my UG Final Year Project - BTech Information Technology.
Evaluation of QoS based Web- Service Selection Techniques for Service Composi...Waqas Tariq
This document discusses quality of service (QoS) based techniques for selecting web services for service composition. It begins by providing background on service-oriented computing and defining service composition. The document then reviews three approaches to web service selection: functional, non-functional, and user-based. It focuses on non-functional (QoS-based) service selection, describing the specifications of QoS-based service selection techniques, including QoS modeling, categorization, user preferences, evaluation criteria, and aggregating evaluation results. The document aims to evaluate various QoS-based service selection techniques and identify criteria for comparing them.
QOS Aware Formalized Model for Semantic Web Service SelectionIJwest
Selecting the most relevant Web Service according to a client requirement is an onerous task, as innumerous number of functionally same Web Services(WS) are listed in UDDI registry. WS are functionally same but their Quality and performance varies as per service providers. A web Service Selection Process involves two major points: Recommending the pertinent Web Service and avoiding unjustifiable web service. The deficiency in keyword based searching is that it doesn’t handle the client request accurately as keyword may have ambiguous meaning on different scenarios. UDDI and search engines all are based on keyword search, which are lagging behind on pertinent Web service selection. So the search mechanism must be incorporated with the Semantic behavior of Web Services. In order to strengthen this approach, the proposed model is incorporated with Quality of Services (QoS) based Ranking of semantic web services.
This paper focuses on various concepts of Quality of Service associated with web services. Various QoS parameters like performance, availability, reliability and stability etc. are formalized in order to enhance the pertinence of web service selection. A QoS mediator agent based Web Service Selection Model is proposed where QoS Consultant acts as a Mediator Agent between clients and service providers. Model suggests user’s preferences on QoS parameter selection. The proposed model helps to select pertinent Web Service as per user’s requirement and reduce the human effort.. Further process of adding ontology with semantic web services is also illustrated here.
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEWijcseit
This document provides a review of web service composition methods and techniques. It begins with an abstract that discusses the increasing need for web service composition as more services become available online. The document then reviews several key concepts related to web service composition, such as ontologies, semantic annotations, quality of service measures, and description languages. It categorizes composition approaches as either syntactic or semantic-based. Syntactic composition relies on syntax alone while semantic composition leverages semantic descriptions. The document discusses various composition methods including manual/static composition, automatic/dynamic composition, and semi-automatic/dynamic or static composition. It provides examples of techniques used for each method. Overall, the document aims to help researchers focus their efforts on developing lasting solutions for
The semantic Web service discovery has been given massive attention within the last few years. With the
increasing number of Web services available on the web, looking for a particular service has become very
difficult, especially with the evolution of the clients’ needs. In this context, various approaches to discover
semantic Web services have been proposed. In this paper, we compare these approaches in order to assess
their maturity and their adaptation to the current domain requirements. The outcome of this comparison
will help us to identify the mechanisms that constitute the strengths of the existing approaches, and
thereafter will serve as guideline to determine the basis for a discovery approach more adapted to the
current context of Web services.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
WEB SERVICE DISCOVERY METHODS AND TECHNIQUES: A REVIEWijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
A new approach to gather similar operations extracted from web servicesIJECEIAES
A web service is an autonomous software that exposes a set of features on the Internet, it is developed and published by providers and accessed by customers who discover it, select it, invoke and use it. Several research policies have been implemented such as searching through keywords, searching according to semantics and searching by estimating the similarity. A customer is looking for a service for the operations he/she carries out, hence the interest of guiding the search for services towards a search for operations: finding the desired operations amounts to finding the services. For this, groupings of similar operations would make it possible to obtain all the services that can meet the desired functionalities. The customer can then select, in this set the service or services according to its non-functional criteria. The paper presents a study of the similarity between operations. The proposed approach is validated through an experimental study conducted on web services belonging to various domains.
SIMILARITY MEASURES FOR WEB SERVICE COMPOSITION MODELSijwscjournal
This document discusses similarity measures for comparing web service composition models. It begins with an abstract that introduces the concept of web service compositions and the need for similarity measures to determine how close one composition is to a given specification. It then provides background on related work involving syntactic, semantic and behavioral similarity measures for web services. The main part of the document proposes a set of similarity measures for comparing web service composition models, including state similarity measures based on state name, type, output parameters, and service type. It represents compositions as finite state machines to define states and transitions. The goal is to measure the degree of closeness between compositions regardless of their modeling language.
Efficient retrieval of web services using prioritization and clusteringAlexander Decker
This document discusses efficient retrieval of web services using prioritization and clustering. It proposes determining dominance relationships among service advertisements that considers multiple parameter degree of match (PDM) criteria. It introduces methods for prioritizing and clustering web services based on similarity measures and efficient algorithms like Transaction Knowledge Data Discovery (TKDD). The implementation retrieves web service description files, captures input/output parameters, compares them using similarity measures like Cosine and Jaccard, computes dominance scores, and prioritizes and clusters the retrieved web services.
IRJET- A Parameter Based Web Service Discovery with Underlying Semantics ProfileIRJET Journal
1. The document discusses various approaches for web service discovery. It describes syntax-based and semantic-based approaches.
2. It outlines several specific approaches for web service discovery including one based on schema matching that uses semantic annotations to enhance discovery. Another approach uses a neural network to calculate semantic similarities between schemas to improve matching.
3. A third approach discusses goal-oriented discovery of resources in RESTful architectures using three layers of abstraction from the agent level to service level to content level.
4. Finally, it introduces the concept of linked services for the web of data by publishing service annotations as linked data and creating services that process and generate linked data.
AGENTS AND OWL-S BASED SEMANTIC WEB SERVICE DISCOVERY WITH USER PREFERENCE SU...IJwest
Service-oriented computing (SOC) is an interdisciplinary paradigm that revolutionizes the very fabric of
distributed software development applications that adopt service-oriented architectures (SOA) can evolve
during their lifespan and adapt to changing or unpredictable environments more easily. SOA is built
around the concept of Web Services. Although the Web services constitute a revolution in Word Wide Web,
they are always regarded as non-autonomous entities and can be exploited only after their discovery. With
the help of software agents, Web services are becoming more efficient and more dynamic.
The topic of this paper is the development of an agent based approach for Web services discovery and
selection in witch, OWL-S is used to describe Web services, QoS and service customer request. We develop
an efficient semantic service matching which takes into account concepts properties to match concepts in
Web service and service customer request descriptions. Our approach is based on an architecture
composed of four layers: Web service and Request description layer, Functional match layer, QoS
computing layer and Reputation computing layer.
Survey on Semantic Web Services and its Composition AlgorithmEditor IJMTER
Service Oriented Architecture (SOA) is a collection of services. The
correspondence of these services takes place with one another. In SOA, Web Services are the
most important & promising part. Web services, adopted by Service Oriented Architecture
(SOA), are loosely coupled reusable software components that semantically encapsulate
discrete functionality and are distributed and programmatically accessible over the internet.
Web Service Composition plays an important role in SOA. Web Service Composition may be
dynamic or static. A composition process requires an algorithm to perform composition task.
Now-a-days various approaches for composition algorithm are used as required by research
task. In this study, we have done a survey on various web service composition algorithms.
The document proposes integrating the IRS-II (Internet Reasoning Service) semantic web services framework with the WSPAB (Web Service Personal Address Book) approach. This would solve limitations of each approach. WSPAB allows classification and selection of services but lacks semantic capabilities. IRS-II allows semantic description and execution of services but lacks user-specific information. The integration would leverage IRS-II's semantic abilities with WSPAB's user profiles to provide personalized, semantically enriched service discovery and selection. The document outlines the components and functioning of each approach, and how their integration could provide users with semantic, personalized web services matching their needs.
This document summarizes an approach to improve personalized ranking using associated edge weights. The key points are:
1) Personalized ranking aims to improve traditional search and retrieval by tailoring results to a user's interests. Authority flow approaches like PageRank and ObjectRank can be used for personalized ranking on entity-relationship graphs.
2) Edge-based personalization assigns different weights to edge types based on the user, affecting authority flow. The paper focuses on improving edge-based personalization by hybridizing the ScaleRank algorithm with k-means clustering.
3) ScaleRank approximates the DataApprox algorithm for ranking. The hybrid approach uses k-means clustering on ScaleRank output to further group related nodes,
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
TOWARDS UNIVERSAL RATING OF ONLINE MULTIMEDIA CONTENTcsandit
Most website classification systems have dealt with the question of classifying websites based on
their content, design, usability, layout and such, few have considered website classification
based on users’ experience. The growth of online marketing and advertisement has lead to
fierce competition that has resulted in some websites using disguise ways so as to attract users.
This may result in cases where a user visits a website and does not get the promised results. The
results are a waste of time, energy and sometimes even money for users. In this context, we design
an experiment that uses fuzzy linguistic model and data mining techniques to capture users’
experiences, we then use the k-means clustering algorithm to cluster websites based on a set of
feature vectors from the users’ perspective. The content unity is defined as the distance between
the real content and its keywords. We demonstrate the use of bisecting k-means algorithm for
this task and demonstrate that the method can incrementally learn from user’s profile on their
experience with these websites.
Web Services Supply Chains: A Literature Reviewijwscjournal
The globalization era has bought almost all services to the internet environment with multiple providers providing the same service in different ways. The same functionality is being offered
by multiple providers so it becomes necessary to differentiate one self from the crowd in order to thrive against the cut throat competition from the service provider’s view. On the other hand it is absolutely essential for the client to select a service provider who meets not only the functional requirements of the client but also provides the best possible quality of service (QoS) to the
customers. The services industry has contributed approximately 55.3% to the GDP of India in the year 2009. The service sector employs about 34% of the labor force in India [CIA report]. The
exponentially growing service industry has started making its global presence through the internet enabled online environment. The web environment has made almost all kind of services available online be it a simple movie ticket booking or the most complex processes such as outsourcing, funds transfer and others. The online services are special kind of services as compared to the static or product supply chain which is more offline or the semi online service supply chains in
which part of the service is offline and partly online.
Cluster based approach for Service Discovery using Pattern RecognitionYogesh Santhan
Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
A review on framework and quality of service based web services discoveryMustafa Algaet
In consequence these services are nowadays accessible to the final clients. In the last few years, more
and more Web Services providing the same functionalities are available in the environment. In order to
select the best service adapted to client’s requests, we need some method capable to evaluate and
compare different services providing the same functionalities. In this context, Quality of service can be defined as the capability to respond to the requirements (constraints) of a client and to fulfill these
needs with the best criteria (preferences) established by the client. It is calculated based on the non-
functional properties of the service. This paper provides an overview of a research progress in Quality
of Service Based Web Services Discovery; it also highlights the issues that need to be investigated in
Quality of Service Based Web Services
Project - UG - BTech IT - Cluster based Approach for Service Discovery using ...Yogesh Santhan
Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
This is my UG Final Year Project - BTech Information Technology.
Evaluation of QoS based Web- Service Selection Techniques for Service Composi...Waqas Tariq
This document discusses quality of service (QoS) based techniques for selecting web services for service composition. It begins by providing background on service-oriented computing and defining service composition. The document then reviews three approaches to web service selection: functional, non-functional, and user-based. It focuses on non-functional (QoS-based) service selection, describing the specifications of QoS-based service selection techniques, including QoS modeling, categorization, user preferences, evaluation criteria, and aggregating evaluation results. The document aims to evaluate various QoS-based service selection techniques and identify criteria for comparing them.
QOS Aware Formalized Model for Semantic Web Service SelectionIJwest
Selecting the most relevant Web Service according to a client requirement is an onerous task, as innumerous number of functionally same Web Services(WS) are listed in UDDI registry. WS are functionally same but their Quality and performance varies as per service providers. A web Service Selection Process involves two major points: Recommending the pertinent Web Service and avoiding unjustifiable web service. The deficiency in keyword based searching is that it doesn’t handle the client request accurately as keyword may have ambiguous meaning on different scenarios. UDDI and search engines all are based on keyword search, which are lagging behind on pertinent Web service selection. So the search mechanism must be incorporated with the Semantic behavior of Web Services. In order to strengthen this approach, the proposed model is incorporated with Quality of Services (QoS) based Ranking of semantic web services.
This paper focuses on various concepts of Quality of Service associated with web services. Various QoS parameters like performance, availability, reliability and stability etc. are formalized in order to enhance the pertinence of web service selection. A QoS mediator agent based Web Service Selection Model is proposed where QoS Consultant acts as a Mediator Agent between clients and service providers. Model suggests user’s preferences on QoS parameter selection. The proposed model helps to select pertinent Web Service as per user’s requirement and reduce the human effort.. Further process of adding ontology with semantic web services is also illustrated here.
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEWijcseit
This document provides a review of web service composition methods and techniques. It begins with an abstract that discusses the increasing need for web service composition as more services become available online. The document then reviews several key concepts related to web service composition, such as ontologies, semantic annotations, quality of service measures, and description languages. It categorizes composition approaches as either syntactic or semantic-based. Syntactic composition relies on syntax alone while semantic composition leverages semantic descriptions. The document discusses various composition methods including manual/static composition, automatic/dynamic composition, and semi-automatic/dynamic or static composition. It provides examples of techniques used for each method. Overall, the document aims to help researchers focus their efforts on developing lasting solutions for
The semantic Web service discovery has been given massive attention within the last few years. With the
increasing number of Web services available on the web, looking for a particular service has become very
difficult, especially with the evolution of the clients’ needs. In this context, various approaches to discover
semantic Web services have been proposed. In this paper, we compare these approaches in order to assess
their maturity and their adaptation to the current domain requirements. The outcome of this comparison
will help us to identify the mechanisms that constitute the strengths of the existing approaches, and
thereafter will serve as guideline to determine the basis for a discovery approach more adapted to the
current context of Web services.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
WEB SERVICE DISCOVERY METHODS AND TECHNIQUES: A REVIEWijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
A new approach to gather similar operations extracted from web servicesIJECEIAES
A web service is an autonomous software that exposes a set of features on the Internet, it is developed and published by providers and accessed by customers who discover it, select it, invoke and use it. Several research policies have been implemented such as searching through keywords, searching according to semantics and searching by estimating the similarity. A customer is looking for a service for the operations he/she carries out, hence the interest of guiding the search for services towards a search for operations: finding the desired operations amounts to finding the services. For this, groupings of similar operations would make it possible to obtain all the services that can meet the desired functionalities. The customer can then select, in this set the service or services according to its non-functional criteria. The paper presents a study of the similarity between operations. The proposed approach is validated through an experimental study conducted on web services belonging to various domains.
SIMILARITY MEASURES FOR WEB SERVICE COMPOSITION MODELSijwscjournal
This document discusses similarity measures for comparing web service composition models. It begins with an abstract that introduces the concept of web service compositions and the need for similarity measures to determine how close one composition is to a given specification. It then provides background on related work involving syntactic, semantic and behavioral similarity measures for web services. The main part of the document proposes a set of similarity measures for comparing web service composition models, including state similarity measures based on state name, type, output parameters, and service type. It represents compositions as finite state machines to define states and transitions. The goal is to measure the degree of closeness between compositions regardless of their modeling language.
Similarity measures for web service composition modelsijwscjournal
A Web service composition is an interconnected set of multiple specialized Web service operations, which
complement each other to offer an improved tool capable of solving more complex problems. Manual
design and implementation of Web service compositions are among the most difficult and error prone tasks.
To face this complexity and to reduce errors at design time, the developer can alternatively search and
reuse existing compositions that have solved similar problems. Thus the problem of designing and
implementing Web service compositions can be reduced to the problem of finding and selecting the
composition closest to an initial specification. To achieve this goal, there is the need to define and use
similarity measures to determine how close is a given composition with respect to any given specification.
Comparison of Web service compositions can be done using two possible sources: composition designs
(models), and execution logs of compositions. In particular, in this paper a set of similarity measures are
described for Web service composition models. The main objective is to measure and assess the degree of
closeness between two given compositions of Web services regardless of their modelling language.
AN ARCHITECTURE FOR WEB SERVICE SIMILARITY EVALUATION BASED ON THEIR FUNCTION...ijwscjournal
By increasing popularity of SOC, using Web services in applications has increased too. SOC creates a loosely coupled environment in which the actual execution environment might differ significantly from the one with the presupposed conditions during application design. Therefore, although an appropriate Web service might have been selected, by passing time, the Web service may not be efficient enough or may not be applicable under specific conditions.
For service-oriented systems to be flexible and self-adaptive, it is necessary to automatically select and use a similar service instead of the one which causes the above mentioned problems. Finding a similar service means specifying the proper services which fulfill the same requirements as those fulfilled by the problematic service.
In most of the previous works, a number of the best services (k) are selected and ordered based on functional similarity. The user must select one of these services based on his/her preferences. One important metric in selecting a similar service is considering QoS properties and user preferences about QoS. Because of the importance of this issue, in the present paper, an architecture is proposed in which, in addition to functional similarity, QoS properties and user preferences are also considered in selecting a similar service.
AN ARCHITECTURE FOR WEB SERVICE SIMILARITY EVALUATION BASED ON THEIR FUNCTION...ijwscjournal
By increasing popularity of SOC, using Web services in applications has increased too. SOC creates a loosely coupled environment in which the actual execution environment might differ significantly from the one with the presupposed conditions during application design. Therefore, although an appropriate Web service might have been selected, by passing time, the Web service may not be efficient enough or may not be applicable under specific conditions.
For service-oriented systems to be flexible and self-adaptive, it is necessary to automatically select and use a similar service instead of the one which causes the above mentioned problems. Finding a similar service means specifying the proper services which fulfill the same requirements as those fulfilled by the problematic service.
In most of the previous works, a number of the best services (k) are selected and ordered based on functional similarity. The user must select one of these services based on his/her preferences. One important metric in selecting a similar service is considering QoS properties and user preferences about QoS. Because of the importance of this issue, in the present paper, an architecture is proposed in which, in addition to functional similarity, QoS properties and user preferences are also considered in selecting a similar service.
This document describes SWORD, a toolkit for composing web services. SWORD represents each service as a rule specifying its inputs and outputs. When a user wants to create a composite service, SWORD uses a rule engine to determine if it can be realized by composing existing services. If so, SWORD generates a composition plan specifying the sequence of services to invoke. SWORD has been implemented in a prototype and can compose information-providing services like those providing data about people, movies, etc. without requiring emerging standards like WSDL or SOAP.
A Clustering Based Collaborative and Pattern based Filtering approach for Big...IIRindia
With web services developing and aggregating in application range, benefit revelation has turned into a hot issue for benefit organization and service management. Service clustering gives a promising approach to part the entire seeking space into little areas in order to limit the disclosure time successfully. In any case, semantic data is a basic component amid the entire arranging process. Current industrialized Web Service Portrayal Language (WSPL) does not contain enough data for benefit depiction. Thusly, a service clustering technique has been proposed, which upgrades unique WSPL report with semantic data by methods for Connected Open Information (COI). Examination based genuine service information has been performed, and correlation with comparable techniques has additionally been given to exhibit the adequacy of the strategy. It is demonstrated that using semantic data from COI improves the exactness of service grouping. Furthermore, it shapes a sound base for promote thorough preparing with semantic data.
RECOMMENDATION FOR WEB SERVICE COMPOSITION BY MINING USAGE LOGSIJDKP
Web service composition has been one of the most researched topics of the past decade. Novel methods of
web service composition are being proposed in the literature include Semantics-based composition, WSDLbased
composition. Although these methods provide promising results for composition, search and
discovery of web service based on QoS parameter of network and semantics or ontology associated with
WSDL, they do not address composition based on usage of web service. Web Service usage logs capture
time series data of web service invocation by business objects, which innately captures patterns or
workflows associated with business operations. Web service composition based on such patterns and
workflows can greatly streamline the business operations. In this research work, we try to explore and
implement methods of mining web service usage logs. Main objectives include Identifying usage
association of services. Linking one service invocation with other, Evaluation of the causal relationship
between associations of services.
Web Service Discovery Mechanisms Based on IR ModelsIRJET Journal
This document discusses various approaches for web service discovery that employ information retrieval (IR) methods. It describes five main approaches:
1. Using singular value decomposition (SVD) to find similar services by representing them as vectors and calculating cosine similarity.
2. Applying the vector space model of IR to represent services and queries as vectors and calculate cosine similarity to discover analogous services.
3. Combining the vector space model with a structure matching algorithm to refine service discovery results.
4. Measuring semantic similarity of services instead of structural similarity by representing data types as trees and calculating edit distances.
5. Enhancing service requests and descriptions with ontologies, representing them as vectors using latent semantic
A Framework For Resource Annotation And Classification In BioinformaticsKate Campbell
This document proposes a framework to semi-automatically extract lightweight semantic annotations from textual descriptions of bioinformatics web services. It uses natural language processing techniques to derive service properties, then applies a simultaneous clustering algorithm to identify clusters of highly correlated services and annotations. This helps classify bioinformatics services to boost discovery, while also detecting relationships between annotations to support ontology building. The approach is evaluated on a corpus of bioinformatics service descriptions.
An effective method for clustering-based web service recommendationIJECEIAES
Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.
A Novel Testing Model for SOA based ServicesAbhishek Kumar
This document proposes a novel testing model for generating and selecting test cases for service-oriented architecture (SOA) based applications. The model involves:
1) A service provider publishing a service and its Web Service Description Language (WSDL) file.
2) A service tester generating test cases by applying XML schema constraints to the WSDL and creating a test data set.
3) The tester using a tool like SoapUI to create test suites containing automatically generated test cases based on the service operations.
4) The tester selecting additional test cases to cover any "missed coverage items" revealed when testing new versions of a service against previous test results.
6 ijmecs v7-n1-5 a novel testing model for soa based servicesAbhishek Srivastava
SOA (Service-Oriented Architecture) filled the gap between software and commercial enterprise. SOA integrates multiple web services. We bear to secure the caliber of web services that gives guarantee about what network services work and their output results. There is close to work has to be performed for an automatic test case generation for SOA based services. But, full coverage of XML elements is missing. To the best of our knowledge this all works do not attempt to cover all possible elements of the XML schema presents in the WSDL file. There is also a need to apply different assertions on each service operation for generating the test cases. To overcome this problem we proposed a novel testing model for SOA based application. This new testing model helps us to get the automatic test cases of SOA based application. We build up our new test model with the aid of our proposed test case generation algorithm and test case selection algorithm. In the end, we generate the test suite execution results and find the coverage of XML schema elements present in the WSDL file.
Enhancement in Web Service ArchitectureIJERA Editor
Web services provide a standard means of interoperating between different software applications, running on a
variety of platforms and/or frameworks. Web services are increasingly used to integrate and build business
application on the internet. Failure of web services is not acceptable in many situations such as online banking,
so fault tolerance is a key challenge of web services. This paper elaborates the concept of web service
architecture and its enhancement. Traditional web service architecture lacks facilities to support fault tolerance.
To better cope with the fundamental issues of the traditional client-server based web service architecture, peer to
peer web service architecture have been introduced. The purpose of this paper is to elaborate the architecture,
construction methods and steps of web services and possible weaknesses in scalability and fault tolerance in
traditional client server architecture and a solution for that, peer to peer web service technology has evolved.
The document describes a conceptual model for services in the PLASTIC project. It builds on previous work modeling mobile distributed computing platforms. The conceptual model represents components, services, and their relationships using a UML-like notation. It extends an existing SeCSE conceptual model by introducing new concepts like context, location, adaptation, and relationships between services and software components. The goal is to develop a common vocabulary that all project partners can use to facilitate communication and modeling tasks.
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WEB SERVICES DISCOVERY AND RECOMMENDATION BASED ON INFORMATION EXTRACTION AND SYMBOLIC REPUTATION
1. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
DOI : 10.5121/ijwsc.2013.4101 1
WEB SERVICES DISCOVERY AND RECOMMENDATION
BASED ON INFORMATION EXTRACTION AND SYMBOLIC
REPUTATION
Mustapha AZNAG1
, Mohamed QUAFAFOU1
, Nicolas DURAND1
and Zahi
JARIR2
1
Laboratory of Information and Systems Sciences, Aix-Marseille University, France
{mustapha.aznag,mohamed.quafafou,nicolas.durand}@univ-amu.fr
2
LISI Laboratory, FSSM, University of Cadi Ayyad, Morocco
zahijarir@ucam.ac.ma
ABSTRACT
This paper shows that the problem of web services representation is crucial and analyzes the various
factors that influence on it. It presents the traditional representation of web services considering traditional
textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services
descriptions are dirty and need significant cleaning to keep only useful information. To deal with this
problem, we introduce rules based text tagging method, which allows filtering web service description to
keep only significant information. A new representation based on such filtered data is then introduced.
Many web services have empty descriptions. Also, we consider web services representations based on the
WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called
symbolic reputation, which is computed from relationships between web services. The impact of the use of
these representations on web service discovery and recommendation is studied and discussed in the
experimentation using real world web services.
KEYWORDS
Web services, WSDL file, data representation, information extraction, semantic tagging, symbolic
reputation, discovery and recommendation.
1. INTRODUCTION
Web services1
[1] are defined as a software systems designed to support interoperable machine-
to-machine interaction over a network. They are loosely coupled reusable software components
that encapsulate discrete functionality and are distributed and programmatically accessible over
the Internet. They are self contain, modular business applications that have open, internet-
oriented, standards based interfaces [7]. Different tasks like matching, ranking, discovery and
composition have been intensively studied to improve the general web services management
process. Thus, web services community has proposed different approaches and methods to deal
with these tasks. Empirical evaluations are generally proposed considering different simulation
scenarios. Nowadays, we are moving from web of data to web of services as the number of UDDI
Business Registries (URBs) is increasing. Moreover, the number of host that offer available web
services, through specific engines like Axis2
, is significantly increasing. Consequently, the WSDL
files describing services can be crawled and parsed automatically. Such files contain different
1
http://www.w3.org/standards/webofservices/
2
http://axis.apache.org/axis/
2. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
2
kind of information like textual descriptions, simple and/or complex types, attributes, etc. (see
Section 3 for more details).Contrary to simulation based approaches, real world services inherit
the complexity of the real world. With the increasing number of published Web services
providing similar functionalities, it’s very tedious for a service consumer to make decision to
select the appropriate one according to her/his needs. Therefore mechanisms and techniques are
required to help consumers to make the best choice. In addition, web services use natural
language descriptions (ambiguity of sentences), they are multi-languages and cross-domains. In
this context, the representation of web service becomes a major challenge. Moreover, the quality
of representations has an important impact on services registries that provide mechanisms for web
services discovery, composition and recommendation. These tasks have been intensively studied
in the literature [4, 16, 17, 24, 9, 2, 12]
This paper studies different representations of web services and compares them considering the
following important tasks: discovery and recommendation. The first representation is centred on
textual descriptions of services and their available functions. It is produced from the web service
descriptions and enriched by integrating the descriptions of operations offered by services and the
structural elements of WSDL, for instance simple and/or complex types, attributes. The previous
representation is traditional and well known by the community of Web services. We consider this
representation as a baseline representation(B)for a web service. We introduce two advanced
representations that refine B considering semantic aspect of text and we show their interest inthe
experiments (see Section 7):
• Rules based text tagging (RBTT): a lot of services have very detailed descriptions,
especially when they offer several operations with their own descriptions. The main
question is how to recognize significant parts or entities in the text description and how to
use the filtered information to describe the web service?
All the previous representations are produced from the web service description, i.e, WSDL
content, which represents the provider point of view. Our goal is to infer a web service
representation from the description of its context (neighbour services).
• Symbolic reputation (SR): web services reputation is generally a numeric quantity
computed from user feedback [17]. The representation we consider here is qualitative as
it is produced from the symbolic descriptions of other web services. Consequently, such a
description describes the relationships of a service with others and does not describe the
service itself.
We introduce in this paper methods that compute the different representations to evaluate their
interest for discovery and recommendation of web services. We also describe a general
architecture of a System developed and used in the experimentation.
This paper is organized as follows. Section 2 provides an overview of related work. Section 3
details the WSDL parsing and the information extraction. Section 4 presents the different
representations of web services. The new proposed representations web services (RBTT and SR)
are discussed respectively in Section 4.2 and 4.3. Section 5 presents the general architecture of
our system. Section 6 is dedicated to the web service discovery and recommendation system.
Finally the experimental results are discussed in Section 7 before concluding.
2. RELATED WORK
In this section, we briefly discuss some of the research works related to discovering Web services.
Although various approaches can be used to locate and discover Web services on the web, we
have focused our research on the service reputation and discovery problems. Every web service
3. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
3
associates with a WSDL document that contains the description of the service. A lot of research
efforts have been devoted in utilizing WSDL documents [16, 28, 29, 25, 12, 26].Dong et al. [16]
proposed the Web services search engine Woogle that is capable of providing Web services
similarity search. However, their engine does not adequately consider data types, which usually
reveal important information about the functionalities of Web services [27]. Liu and Wong [25]
apply text mining techniques to extract features such as service content, context, host name, and
name, from Web service description files in order to cluster Web services. They proposed an
integrated feature mining and clustering approach for Web services as a predecessor to discovery,
hoping to help in building a search engine to crawl and cluster non-semantic Web services.
Elgazzar et al. [12] proposed a similar approach, which clusters WSDL documents to improve the
non-semantic web service discovery. They take the elements in WSDL documents as their
feature, and cluster web services into functionality based clusters. The clustering results can be
used to improve the quality of web service search results.
In [30], the authors proposed an architecture for Web services filtering and clustering. The service
filtering mechanism is based on user and application profiles that are described using OWL-S
(Web Ontology Language for Services). The effectiveness of the filters is based on a clustering
analysis that compares services related clusters. The objectives of this matchmaking process are
to save execution time and to improve the refinement of the stored data. Another similar approach
[31] concentrates on Web service discovery with OWL-S. The OWLS is first combined with
WSDL to represent service semantics before using a clustering algorithm to group the collections
of heterogeneous services together. Finally, a user query is matched against the clusters, in order
to return the suitable services. Nevertheless, the creation and maintenance of ontologies may be
difficult and involve a huge amount of human effort [28, 29].
Web service reputation represents a mechanism relying on feedbacks provided by
consumers/software agent to measure Web service trustworthiness. It is modelled as a vector of
aggregate consumers/software agent’s ratings for a web service. Also various rating feedbacks are
aggregated to derive a service provider’s reputation. Generally feedback is composed from
collected quality of service (QoS) information acquired from execution monitoring and those that
require consumer’s intervention like price or accuracy that cannot be monitored. According to the
QoS information published and a consumer preference including required QoS metrics, the QoS
registry will calculate an overall rating for each web service that matches the consumer’s search
request. Then the consumer will select the web service with the highest rating [17]. Most of
proposed reputation approaches use a central registry to collect and share consumer’s feedbacks.
Since this central architecture is subject of failure, other works based on peer-to-peer web
services are proposed to deal with decentralized reputation mechanism [32]. A service provider
that provides satisfactory service may get incorrect or false ratings from unfair or malicious
raters. One of challenging problem is protecting web service reputation from these incorrect
inputs. Some mechanisms have been proposed to detected and deal with dishonest feedbacks by
using dedicated monitoring agents to filter consumers evaluation [33], or collaborative filtering
techniques based on peer to peer solution [34]. In the literature, a rating of a service is a vector of
attribute values. The computed reputation rating may be a binary value (trusted or untrusted), a
scaled integer (e.g. 1-10), or on a continuous scale (e.g., [0, 1]). Therefore the satisfaction level of
web services is generally a normalized numerical value, representing quantitative reputation, used
for dynamic services ranking and selection.
Maximilien and Singh [3, 35] proposed a multi-agent based architecture where agents assist in
quality-based service selection using an agency to disseminate reputation and endorsement
information. Each proxy agent is autonomous but also collaborates with other agents to collect
other opinions and therefore maximizes its information to improve its decision-making. Liu, Ngu
and Zeng [36] proposed an algorithm about how to combine different QoS metrics to get a fair
overall rating for a web service. The proposed reputation can be defined as the average ranking
4. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
4
given to the service by end users. Majithia et al. [5] consider ratings of services in different
contexts and a coefficient (weight) is attached to each particular context. This coefficient reflects
its importance to a particular set of users. Based on this coefficient, they propose a method to
compute the reputation score as weighted sum of ratings for a service. Wish art et al. [37]
introduce an aging factor for the reputation score, which is applied to each of the ratings for a
service. Based on these two works, Xu et al. [17] propose to calculate reputation score as the
weighted average of all ratings of a service received from consumers, including an inclusion
factor representing the weight attached to each of the ratings for the service. Trust and reputation
mechanisms are closely related. Web service reputation can be considered as an aggregation of
evaluation for a service from consumers/software agent, while web service trust represents a
personalized and subjective opinion reflecting a web service [38]. Currently, several researches in
the area of trust and reputation topic are considered. We cite for instance the work [33] that
proposes an algorithm to analyse the trustworthiness for each consumer, which ultimately
facilitates the web services selection process considering feedbacks reported by trusted users than
others.
Web service community has been introduced to easy-of-use web service discovery. It can be
viewed as a mediator that holds the meta-data and registry information about its member services
and represents domain-specific knowledge [39]. Among Community-based approach, we present
for instance the work [34] that has proposed a community-based service selection approach based
on super agents. These agents share their information about services they have interacted with,
which is useful for other agents to make effective selection of services. This in order to maintain
communities and build community-based reputation for a service based on the opinions from all
community members that have similar interests and judgement criteria.
These efforts have an implicit hypothesis, which considers that web service reputation is a
numerical quantity. As far as we know, it’s the first time we break the quantitative reputation
hypothesis. This paper avoids this hypothesis considering the qualitative aspect of the reputation
and describes how to compute and use it.
3. WSDL DOCUMENT PARSING AND INFORMATION EXTRACTION
The Web Service Description Language (WSDL) is an XML-based language, designed according
to standards specified by the W3C that provides a model for describing web services. It describes
one or more services as collections of network endpoints, or ports. It provides the specifications
necessary to use the web service by describing the communication protocol, the message format
required to communicate with the service, the operations that the client can invoke and the service
location. Two versions of WSDL recommendation exist: the 1.13
version, which is used in almost
all existing systems, and the 2.04
version which is intended to replace 1.1. These two versions are
functionally quite similar but have substantial differences in XML structure.
To manage efficiently web services descriptions, we extract all features that describe a web
service from the WSDL document and store them into a relational database. We recognize both
WSDL versions (1.1 and 2.0). During this process, we proceed in three steps (see Figure 1). The
first step consists of checking availability of web service and validating the content of WSDL
document. The second step is to get the WSDL document and read it directly from the WSDLURI
to extract all information of the document. In this step we describe the features to extract from the
WSDL document: (1) the name, the documentation and the version of the WSDL, (2) WSDL
types used by messages to transmit information between web services. Data types are often
3
http://www.w3.org/TR/wsdl
4
http://www.w3.org/TR/wsdl20/
5. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
5
specified using a XML Schema Definition (XSD). We extract all kind of elements and types that
can be as simple or complex types as a set of elements and/or attributes, and (3)a set of services
declared in the WSDL document. For each service we extract the name, the documentation and a
set of endpoints. Then, for each endpoint we extract the name, the address (which defines the
connection point to web service. It is typically represented by a simple HTTP URL) and the
binding(Name, Type, Style, Transport protocol). The binding specifies the interface as well as
defining the SOAP binding style (RPC/Document) and transport SOAP protocol. The interface
defines the operations to be performed for a web service, and the messages that are used to
perform the operation. We also extract for each operation the name, the documentation, the input
and the output parameters. The input/output parameters can be referred to the previously
extracted types/elements. Finally, the third step is dedicated to save the extracted WSDL features
into a database. The extracted information will be used during the generation of
representations(B, RBTT and SR). Before presenting the methods for calculating representations,
we discuss some text-processing standard used thereafter.
1. Tag removal: This step removes all HTML tags, CSS components, symbols(punctuation,
etc.).
2. Splitting and remove a stop words: Some terms are composed by several words
separated by a capital letter; we use therefore regular expression to extract these words.
To illustrate, the application of the regular expression ([A-Z][a-z]+) on this string "GetAll
Country Currencies Response" produces’ Get’, ’All’, ’Country’, ’Currencies’ and
’Response’. Furthermore, to extract the potential content words, we remove all the stop
words. Finally, the potential content words for the previous example are ’Country’ and
’Currencies’.
3. Word Stemming: In this step we use the Porter Stemmer [21] to remove words, which
have the same stem. Words with the same stem will usually have the same meaning. For
example, ’computer’, ’computing’ and ’compute’ have the stem ’comput’.
Figure 1. WSDL document parsing and information extraction
International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
5
specified using a XML Schema Definition (XSD). We extract all kind of elements and types that
can be as simple or complex types as a set of elements and/or attributes, and (3)a set of services
declared in the WSDL document. For each service we extract the name, the documentation and a
set of endpoints. Then, for each endpoint we extract the name, the address (which defines the
connection point to web service. It is typically represented by a simple HTTP URL) and the
binding(Name, Type, Style, Transport protocol). The binding specifies the interface as well as
defining the SOAP binding style (RPC/Document) and transport SOAP protocol. The interface
defines the operations to be performed for a web service, and the messages that are used to
perform the operation. We also extract for each operation the name, the documentation, the input
and the output parameters. The input/output parameters can be referred to the previously
extracted types/elements. Finally, the third step is dedicated to save the extracted WSDL features
into a database. The extracted information will be used during the generation of
representations(B, RBTT and SR). Before presenting the methods for calculating representations,
we discuss some text-processing standard used thereafter.
1. Tag removal: This step removes all HTML tags, CSS components, symbols(punctuation,
etc.).
2. Splitting and remove a stop words: Some terms are composed by several words
separated by a capital letter; we use therefore regular expression to extract these words.
To illustrate, the application of the regular expression ([A-Z][a-z]+) on this string "GetAll
Country Currencies Response" produces’ Get’, ’All’, ’Country’, ’Currencies’ and
’Response’. Furthermore, to extract the potential content words, we remove all the stop
words. Finally, the potential content words for the previous example are ’Country’ and
’Currencies’.
3. Word Stemming: In this step we use the Porter Stemmer [21] to remove words, which
have the same stem. Words with the same stem will usually have the same meaning. For
example, ’computer’, ’computing’ and ’compute’ have the stem ’comput’.
Figure 1. WSDL document parsing and information extraction
International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
5
specified using a XML Schema Definition (XSD). We extract all kind of elements and types that
can be as simple or complex types as a set of elements and/or attributes, and (3)a set of services
declared in the WSDL document. For each service we extract the name, the documentation and a
set of endpoints. Then, for each endpoint we extract the name, the address (which defines the
connection point to web service. It is typically represented by a simple HTTP URL) and the
binding(Name, Type, Style, Transport protocol). The binding specifies the interface as well as
defining the SOAP binding style (RPC/Document) and transport SOAP protocol. The interface
defines the operations to be performed for a web service, and the messages that are used to
perform the operation. We also extract for each operation the name, the documentation, the input
and the output parameters. The input/output parameters can be referred to the previously
extracted types/elements. Finally, the third step is dedicated to save the extracted WSDL features
into a database. The extracted information will be used during the generation of
representations(B, RBTT and SR). Before presenting the methods for calculating representations,
we discuss some text-processing standard used thereafter.
1. Tag removal: This step removes all HTML tags, CSS components, symbols(punctuation,
etc.).
2. Splitting and remove a stop words: Some terms are composed by several words
separated by a capital letter; we use therefore regular expression to extract these words.
To illustrate, the application of the regular expression ([A-Z][a-z]+) on this string "GetAll
Country Currencies Response" produces’ Get’, ’All’, ’Country’, ’Currencies’ and
’Response’. Furthermore, to extract the potential content words, we remove all the stop
words. Finally, the potential content words for the previous example are ’Country’ and
’Currencies’.
3. Word Stemming: In this step we use the Porter Stemmer [21] to remove words, which
have the same stem. Words with the same stem will usually have the same meaning. For
example, ’computer’, ’computing’ and ’compute’ have the stem ’comput’.
Figure 1. WSDL document parsing and information extraction
6. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
6
4. REPRESENTATIONS OF WEB SERVICES
In this section, we present a generation method of the traditional representation of web service
and we introduce two new representations. Let us note that the generated representations are
vectorial.
4.1. Baseline representation (B)
A web service can be described by a textual description extracted from WSDL document or given
by its provider when publishing in the UDDI. The current UDDI registry only allows searching
web services by their textual description. The first representation is centred on textual
descriptions of services and their offered functions. This is produced from the web service
descriptions and enriched by integrating the descriptions of operations offered by services. Let us
remark that the major disadvantage is that most web services have a poor or an empty description.
To complete this representation, we added all information described by WSDL types. The types
are used by messages to transmit information between web services. Consequently, WSDL types
are good features to describe the functionality of a service and are the most informative element
in WSDL document. For this reason, we extract all type names (elements, complex types, simple
types, attributes, enumerations) and we apply the textual processing (see Section 3 -steps 2 and 3)
to produce a set of words. Thus, we use the obtained set of words to construct the new
representation and we consider it as a baseline representation (B) for a web service.
4.2. Rules Based Text Tagging of web services descriptions (RBTT)
A lot of services have very detailed descriptions, especially when they offer several operations
with their own descriptions. The main question is how to recognize significant parts or entities in
the text description and how to use the filtered information to describe the web service? Our
approach consists in the definition of extraction rules to identify, extract and annotate relevant
multi-word terms from web service descriptions. This approach has been already used for
biological data [19]. The processing steps(Tokenization, Part-Of-Speech tagging, Extraction and
output generation) of Information Extraction have been implemented as modules using the
Lingua Stream platform5
. Lingua Stream [20] is an integrated experimentation environment
targeted to researchers in natural language processing (NLP). Let us note that we used Tree
Tagger6
for the Part-Of-Speech tagging step. The extracted information is given in a form of a
XML file. In the context of the web service descriptions, we have defined a set of rules into
Prolog reflecting the Definite Clause Grammar (DCG) to recognize 3types of information: web
service names (namews), purposes of the web services(purpose), and the domain of utilization
(domain). Let us remark that we do not use patterns in the sense of Information Extraction that is
without a prior on the form of the expressions.
Figure 2. Structure of rules
Figure 2 presents the structure of the rules. From a ’context’, an expression(generally a multi-
word term, a nominal phrase) is recognized until a stop phrase is encountered. The context is a set
5
http://www.linguastream.org/
6
http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/DecisionTreeTagger.html
International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
6
4. REPRESENTATIONS OF WEB SERVICES
In this section, we present a generation method of the traditional representation of web service
and we introduce two new representations. Let us note that the generated representations are
vectorial.
4.1. Baseline representation (B)
A web service can be described by a textual description extracted from WSDL document or given
by its provider when publishing in the UDDI. The current UDDI registry only allows searching
web services by their textual description. The first representation is centred on textual
descriptions of services and their offered functions. This is produced from the web service
descriptions and enriched by integrating the descriptions of operations offered by services. Let us
remark that the major disadvantage is that most web services have a poor or an empty description.
To complete this representation, we added all information described by WSDL types. The types
are used by messages to transmit information between web services. Consequently, WSDL types
are good features to describe the functionality of a service and are the most informative element
in WSDL document. For this reason, we extract all type names (elements, complex types, simple
types, attributes, enumerations) and we apply the textual processing (see Section 3 -steps 2 and 3)
to produce a set of words. Thus, we use the obtained set of words to construct the new
representation and we consider it as a baseline representation (B) for a web service.
4.2. Rules Based Text Tagging of web services descriptions (RBTT)
A lot of services have very detailed descriptions, especially when they offer several operations
with their own descriptions. The main question is how to recognize significant parts or entities in
the text description and how to use the filtered information to describe the web service? Our
approach consists in the definition of extraction rules to identify, extract and annotate relevant
multi-word terms from web service descriptions. This approach has been already used for
biological data [19]. The processing steps(Tokenization, Part-Of-Speech tagging, Extraction and
output generation) of Information Extraction have been implemented as modules using the
Lingua Stream platform5
. Lingua Stream [20] is an integrated experimentation environment
targeted to researchers in natural language processing (NLP). Let us note that we used Tree
Tagger6
for the Part-Of-Speech tagging step. The extracted information is given in a form of a
XML file. In the context of the web service descriptions, we have defined a set of rules into
Prolog reflecting the Definite Clause Grammar (DCG) to recognize 3types of information: web
service names (namews), purposes of the web services(purpose), and the domain of utilization
(domain). Let us remark that we do not use patterns in the sense of Information Extraction that is
without a prior on the form of the expressions.
Figure 2. Structure of rules
Figure 2 presents the structure of the rules. From a ’context’, an expression(generally a multi-
word term, a nominal phrase) is recognized until a stop phrase is encountered. The context is a set
5
http://www.linguastream.org/
6
http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/DecisionTreeTagger.html
International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
6
4. REPRESENTATIONS OF WEB SERVICES
In this section, we present a generation method of the traditional representation of web service
and we introduce two new representations. Let us note that the generated representations are
vectorial.
4.1. Baseline representation (B)
A web service can be described by a textual description extracted from WSDL document or given
by its provider when publishing in the UDDI. The current UDDI registry only allows searching
web services by their textual description. The first representation is centred on textual
descriptions of services and their offered functions. This is produced from the web service
descriptions and enriched by integrating the descriptions of operations offered by services. Let us
remark that the major disadvantage is that most web services have a poor or an empty description.
To complete this representation, we added all information described by WSDL types. The types
are used by messages to transmit information between web services. Consequently, WSDL types
are good features to describe the functionality of a service and are the most informative element
in WSDL document. For this reason, we extract all type names (elements, complex types, simple
types, attributes, enumerations) and we apply the textual processing (see Section 3 -steps 2 and 3)
to produce a set of words. Thus, we use the obtained set of words to construct the new
representation and we consider it as a baseline representation (B) for a web service.
4.2. Rules Based Text Tagging of web services descriptions (RBTT)
A lot of services have very detailed descriptions, especially when they offer several operations
with their own descriptions. The main question is how to recognize significant parts or entities in
the text description and how to use the filtered information to describe the web service? Our
approach consists in the definition of extraction rules to identify, extract and annotate relevant
multi-word terms from web service descriptions. This approach has been already used for
biological data [19]. The processing steps(Tokenization, Part-Of-Speech tagging, Extraction and
output generation) of Information Extraction have been implemented as modules using the
Lingua Stream platform5
. Lingua Stream [20] is an integrated experimentation environment
targeted to researchers in natural language processing (NLP). Let us note that we used Tree
Tagger6
for the Part-Of-Speech tagging step. The extracted information is given in a form of a
XML file. In the context of the web service descriptions, we have defined a set of rules into
Prolog reflecting the Definite Clause Grammar (DCG) to recognize 3types of information: web
service names (namews), purposes of the web services(purpose), and the domain of utilization
(domain). Let us remark that we do not use patterns in the sense of Information Extraction that is
without a prior on the form of the expressions.
Figure 2. Structure of rules
Figure 2 presents the structure of the rules. From a ’context’, an expression(generally a multi-
word term, a nominal phrase) is recognized until a stop phrase is encountered. The context is a set
5
http://www.linguastream.org/
6
http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/DecisionTreeTagger.html
7. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
7
of ’trigger’ words. The stop phrases can be words, symbols, verbs, punctuation, etc. They depend
on the rule type.
Currently, the identification of the context has been done manually, however an automatic
learning of the context can also be considered. We have manually detected special phrases for
each type of information on an excerpt of the corpus. We have noted the corresponding trigger
words and the stop phrases. The common stop phrases are the trigger words, some punctuation
symbols and the relative pronouns. The current rule base consists of 35 rules – 13 for namews,16
for purpose, and 6 for domain. Let us note that different cases can be expressed by the same rule.
Let us take some examples.
Example 1:The web service description is ”CAPTCHA-image web service for serving and
validating CAPTCHA-images ”. We obtain this result:
<ws><id>WSID 172</id>
<namews>CAPTCHA-image</namews></ws>
Example 2: The web service description is ”The ICD9 coding system is an international
classification system which groups related disease entities and procedures for the purpose of
reporting statistical information. The system is widely used to for medical billing ”. We obtain
this result:
<ws><id>WSID 29</id>
<domain>medical billing</domain></ws>
In the example 1, the term ”CAPTCHA-image” is extracted by using the following set of rules:
Webservname (type:namews..name:N) --> start, nws(N).
start --> @lemma:’.’ ; ls_lookupToken(_,_,startPara).
nws(N) -->ls_token(N,tag:X,_), end, {not(member(X,[dt]))}.
nws(N) -->ls_token(N1,_), nws(N2), {concat(N1,N2,N)}.
end --> @lemma:web, @lemma:service ; @lemma:webservice.
The trigger phrase is the start of a paragraph (”startPara ”) or the start of a new sentence. The
rules ”nws ” allow the system to recognize the multiword terms. The end phrase is here Web
Service or Web Service (with or with out capitals). Let us remark that the rule ”nws ” avoid the
recognition of a single determiner (”dt ”) just before the end phrase.
Our system differs from the classical approaches. From Information Extraction method point of
view, simple declarative extraction rules are designed, making the implementation process ”light
and quick”. The rules are domain-specific, but by no means corpus-specific. Moreover, our
method is endogenous: no resources such as knowledge base or dictionary are needed at the
beginning. There sources are constructed on the fly – the system learns new terms (which can be
new terms in the domain or missing) to be used later or in other corpora and/or in other text
mining applications.
All the previous representations are produced from the web service description, i.e, WSDL
content, which represents the provider point of view. Our goal is to infer a web service
representation from the description of its context (neighbour services).
4.3. Symbolic reputation model (SR)
The reputation of a web service reflects a common perception of other web services or customers
towards that service. In other words, it aggregates the web service ratings given by consumers.
8. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
8
Typically, a reputation would be built from a history of ratings. Several reputation systems have
been proposed in the literature [17, 3, 5, 13]. However, feedbacks from users are not easily
available and Hidden Markov Models (HMM) were used to predict the reputation of a service
provider [10]. In [8], the authors propose an algorithm for generating locally calculated reputation
ratings from a semantic network. This effort has an implicit hypothesis, which considers that web
service reputation is a numerical quantity. As far as we know, it is the first time we go beyond the
quantitative reputation by proposing a symbolic reputation notion. In fact, our work considers the
qualitative aspect of the reputation and describes how to compute and use it. In this paper, we
consider that the numeric reputation of a service s, denoted Rnum(s), results from customer’s
feedback and computed as the weighted average of all ratings the service received from
customers [17]:
Rnum (s) =
si
∗ l di
i=1
N s
∑
l di
i=1
N s
∑
(1)
Where Ns is the number of ratings for the service s, si
is the ithservice rating, l ∈[0,1]is the
inclusion factor tacking into account the dimension time (smaller lmeans that the more recent
ratings have a larger impact in the reputation score), and di is the age of the ithservice rating.
Before computing symbolic reputation, we explain the structure of the web service space by
modelling relationships between services. Web services dependency graph was used to represent
the structure of the web services space, i.e. web services relationships, by generating a network
depending on the Input / Output of each web service. This induced network is then used for web
service composition [23,2]. The general approach consists of searching first for web services
operations dependencies using the I/O parameters of their operations then the web services
dependency graph is induced. For seek of simplicity, we consider here that a dependency occurs
between two services when they offer at least two dependent operations: a service requires (resp.
provides) data from (resp. to) another service. of course, other dependency definitions can be used
without affecting our general symbolic reputation computing approach.
Definition 4.3.1 Operations dependency:
Let F ={f1, f2,..., fn} be the set of operations offered by all web services published in the UDDI.
InF(fi ),OutF(fi )represent respectively the inputs and the outputs of the operation fi ,
a ∈[0,1] a given threshold and simthe similarity function.
The operation fj depends on the operation fi , denoted fi → fj , if and only if
∀p ∈ InF( fj ),∃q ∈ OutF( fi ):sim(p,q)≥a . sim(a,b) =1− NGD(a,b)is the featureless
similarity factor computed between words a and b using Normalized Google Distance (NGD)
[22] as a featureless distance measure between words.
Definition 4.3.2 Web services dependency graph:
Let S ={s1,s2,...,sn} be the web services space containing all services published in the UDDI,
we define the dependency graph as the directed graph G = (S,V)where
V ={(si,sj ) ∈ S×S,∃fi ∈ si,∃fj ∈ sj : fi → fj }. We note f ∈ s when the service soffers the
operation f .
9. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
9
This construction of the direct web services dependency graph is needed to compute the symbolic
reputation. Our model of symbolic reputation is based on random walks models. A random walk
is a sequence of nodes in a graph constructed by the following process: select a random starting
node in the graph, then move to select a neighbour of this node at random and so forth. The
random walk analysis has been applied to different fields as a fundamental model for random
processes in time [11]. More particularly, a formal model was proposed in [6] to allow the
computation of the reputation of a web page using the web hyperlink structure. We have adapted
this model to compute the symbolic reputation of web services.
We consider the constructed dependency graph. The probability of visiting a service s for term t
at step nof the walk, denoted Pn
(s,t), is defined as follows:
Pn
(s,t) = (1− d)
Pn−1
(q,t)
O(q)q→s
∑ +
d
Nt
if t ∈ VR(q)
0 Otherwise
(2)
The previous probability is the core of the algorithm that computes the symbolic reputation. In
fact, suppose that with the probability d the random walker jumps into a service uniformly
chosen at random from the set of services that contains the term t . In this context, the probability
that a random walker visits a service s in a random jump is
d
Nt
( Nt denotes the total number of
services on the UDDI that contains the term t ) if the vectorial representation VR(q)of service q
contains the term t and it is zero otherwise. The probability that the walker visits the service s at
step nafter visiting one among its parent service qis
1− d
O(q)
Pn−1
(q,t)where Pn−1
(q,t) denotes
the probability that the walker visits the service q for term t at step n −1 and O(q)denotes the
number of services that require data from q (parents of s in the dependency graph). Let us
denoteVR(s), the vectorial representation of service s. Algorithm 1 gives the details on the
computation of the symbolic reputation of a given service.
5. GENERAL ARCHITECTURE AND UDDI EXTENSION
The aim of our implementation is to extend a UDDI by introducing the different approaches
previously discussed. Our system supports service consumers to discover web services that meet
their requirements. It also recommends other services to customers using symbolic reputation. In
addition, our platform allows enriching the UDDI register automatically by collecting Web
services published in others UDDI registries. In our implementation, services or agents (dynamic
Web services) of our system reside in a remote server that consumers know a priori. Our system
enables service providers to publish their services with QoS (price, availability, response time, . .
.) and consumers to find web services that meet their functional requirements and/or QoS
requirements. Figure 3 gives an overview of the general architecture by outlining the different
components and their relationships. The general architecture of our system consists of the
following components:
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10
ALGORITHM 1: SYMBOLIC REPUTATION COMPUTING
REQUIRE:
• d: The random surfer jump.
• k: Maximum number of iteration.
• s: A given service.
ENSURE: SR(s): Symbolic reputation of the service s.
1: SR(s) = ∅
2: VR(s)= get Vectorial Representation(s)
3: FORALL term t ∈ VR(s)DO
4: P(s,t)= d / Nt
5: ENDFOR
6: FOR l =1,2,..,kDO
7: IF l < kTHEN
8: d'
= d
9: ELSE
10: d'
=1
11: ENDIF
12: FOR every path ql →...→q1 →sof length l and every term t in VR(ql )DO
13: P(s,t) = 0if term t has not been seen before
14: P(s,t) = P(s,t)+[(1−d)l
/ O(qii=1
l
∏ )(d'
/ Nt )
15: ENDFOR
16: ENDFOR
17: FOR every term t with P(s,t)>1/ Nt DO
18: SR(s) = SR(s)∪t
19: ENDFOR
20: RETURN SR(s)
• UDDI Registry: It allows service providers to publish their web services.
• UDDI Manager: It contains two interfaces Service Publisher and Service Finder.
Service Publisher offers all the necessary operations to service providers to manage their
services in the UDDI registry. This is the only possible way to interact with the UDDI
registry. Service Finder allows finding services according to functional requirements. It
can also provide information that describes a web service published in the UDDI registry.
• Discovery Agent: It receives consumer’s requests, finds the web services that meet their
requirements and sends the response to the customer. It also collects feedback rating from
customers expressed as a score reflecting their level of satisfaction after interacting with
the discovered service.
• Reputation Manager: It allows calculating the symbolic reputation of web services.
• Clustering Manager: It allows creating the clusters of web services.
• WSDL Manager: It allows parsing, extracting features from WSDL documents and save
them in the WSDB database.
• WVMC rawler: It is a dynamic web service that runs automatically to collect Web
services published in other UDDI registries.
• WSDB Manager: It allows managing the WSDB database (CRUD -
Create/Read/Update/Delete operations).
11. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
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Figure 3. General architecture and UDDI extension
(a). Services register.
(b). Register / unregister services and business on UDDI Registry.
(c). Save the QoS advertisements (price, availability, response time, . . .) if are specified for
services to register on database WSDB.
(d). CRUD (Create/Read/Update/Delete) operations on database WSDB.
(e). Request/Response.
(f). Find services from UDDI by functional requirements.
(g). (1) Get Qos and reputation advertisements for services that functional requirements
match with user’s query. (2) Send user’s feedback score for web service.
(h). Bind a web service.
(i). Get services from UDDI and calculate the Quantitative/Qualitative reputation.
(j). Save the computed reputation for each service on WSDB.
(k). Get services from UDDI and construct the clusters of Web services.
(l). Save the constructed clusters on WSDB.
(m). Get the new WSDL URI published on UDDI Registry and parse it to extract all
information of WSDL document.
(n). Save the features extracted by WSDL Parser on WSDB.
(o). Crawler WSDLs URI from others UDDI registries.
(p). Register the new crawled web services on UDDI registry.
6. WEB SERVICE DISCOVERY AND RECOMMENDATION SYSTEM
The proposed web service discovery and selection algorithm is based on the three following
operators: Matching, Ranking and Selection (see Algorithm 2):
International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
11
Figure 3. General architecture and UDDI extension
(a). Services register.
(b). Register / unregister services and business on UDDI Registry.
(c). Save the QoS advertisements (price, availability, response time, . . .) if are specified for
services to register on database WSDB.
(d). CRUD (Create/Read/Update/Delete) operations on database WSDB.
(e). Request/Response.
(f). Find services from UDDI by functional requirements.
(g). (1) Get Qos and reputation advertisements for services that functional requirements
match with user’s query. (2) Send user’s feedback score for web service.
(h). Bind a web service.
(i). Get services from UDDI and calculate the Quantitative/Qualitative reputation.
(j). Save the computed reputation for each service on WSDB.
(k). Get services from UDDI and construct the clusters of Web services.
(l). Save the constructed clusters on WSDB.
(m). Get the new WSDL URI published on UDDI Registry and parse it to extract all
information of WSDL document.
(n). Save the features extracted by WSDL Parser on WSDB.
(o). Crawler WSDLs URI from others UDDI registries.
(p). Register the new crawled web services on UDDI registry.
6. WEB SERVICE DISCOVERY AND RECOMMENDATION SYSTEM
The proposed web service discovery and selection algorithm is based on the three following
operators: Matching, Ranking and Selection (see Algorithm 2):
International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
11
Figure 3. General architecture and UDDI extension
(a). Services register.
(b). Register / unregister services and business on UDDI Registry.
(c). Save the QoS advertisements (price, availability, response time, . . .) if are specified for
services to register on database WSDB.
(d). CRUD (Create/Read/Update/Delete) operations on database WSDB.
(e). Request/Response.
(f). Find services from UDDI by functional requirements.
(g). (1) Get Qos and reputation advertisements for services that functional requirements
match with user’s query. (2) Send user’s feedback score for web service.
(h). Bind a web service.
(i). Get services from UDDI and calculate the Quantitative/Qualitative reputation.
(j). Save the computed reputation for each service on WSDB.
(k). Get services from UDDI and construct the clusters of Web services.
(l). Save the constructed clusters on WSDB.
(m). Get the new WSDL URI published on UDDI Registry and parse it to extract all
information of WSDL document.
(n). Save the features extracted by WSDL Parser on WSDB.
(o). Crawler WSDLs URI from others UDDI registries.
(p). Register the new crawled web services on UDDI registry.
6. WEB SERVICE DISCOVERY AND RECOMMENDATION SYSTEM
The proposed web service discovery and selection algorithm is based on the three following
operators: Matching, Ranking and Selection (see Algorithm 2):
12. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
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• Matching: Find services that meet the consumer’s functional requirements. Then
maintain services that meet only the non-functional requirements (QoS and/or
reputation).
• Ranking: Calculate an overall score that combines the quality of service(availability,
response time, . . .) and/or reputation score and use it to classify the web services,
• Selection: Select only the best services according to the classification results and the
maximum number of services specified by consumer, and return them.
Algorithm 2 shows the details of the discovery process and selection of our system. The
functional matching process is based on the consumer’s functional requirements. The Qos
matching process is based on the algorithm proposed by Maximilien and Singh [4] and improved
by Xu et al. [17].A service consumer sends a service discovery request to the Discovery Agent
(see Figure 3), which then contacts the UDDI Manager and WSDB Manager to find the services
that meet the functional requirements (func Matching() - line 1).If no matched service is found,
the Discovery Agent returns an empty result to the consumer. If multiple services match the
functional requirements the discovery service contacts the WSDB Manager to retrieve the Qos
attributes for candidates services previously selected. If multiple services match the Qos
requirements (Qos Matching() - line 5) the discovery agent calculates the Qos score for each
service. If the consumer’s reputation requirement is specified, the discovery agent contacts the
WSDB Manager to retrieve the reputation score for each candidate services. Then the ranking
process (Ranking() - line 8) calculates the overall score and uses it to rank the candidates
services. If no reputation requirement is specified the ranking process (Ranking - line 12) uses
only the computed Qos score. Finally, the selection process (Select) selects nbMax services with
the highest scores and returns them to the consumer (nb Maxdenotes the maximum number of
service to be returned as specified by the consumer). If nbMax is not specified, one service is
randomly selected from services whose Qos score (or overall score) is greater than the given
threshold.
We also propose an algorithm for web services recommendation using the symbolic reputation
(SR). Algorithm 3 shows the details of the symbolic reputation based recommendation. The
general method is based on the following steps: (1) we use Algorithm 2 to discover services that
match consumer’s requirements.(2) We retrieve the symbolic reputation SR(s) for each
discovered services. Then, we find all services q that have a vectorial representation VR(q) such
that SR(s)∩VR(q) ≠ ∅and its cardinality is greater than a given thresholda . Then, the ranking
process (Ranking - line 9) uses the computed Qos score and the reputation score to rank the
candidates services previously selected. Finally, the selection process (Select - line 10) selects
the services with the highest scores and returns them to the consumer. Only the best services
according to the ranking result are recommended.
ALGORITHM 2: WEB SERVICE DISCOVERY AND SELECTION
REQUIRE:
• fRq: Functional requirements
• qosRq: Qos requirements
• repRq: Reputation requirements
• nbMax: Maximum number of services to be returned
ENSURE: select: A set of discovered services
13. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
13
1: Service[] f Match= func Matching(fRq)
2: select = Service[]
3: qMatch = Service[]
4: IFqosRq != null THEN
5: qMatch= Qos Matching(f Match,qosRq)
6: sRank = Service[]
7: IFrepRq != null THEN
8: sRank= Ranking(qMatch, qosRq, repRq)
9: select = Select(sRank,nbMax,"QosAndRep")
10: RETURN select
11: ELSE
12: sRank= Ranking (qos Match, qosRq)
13: select= Select (sRank, nbMax, "Qos")
14: RETURN select
15: ENDIF
16: ELSE
17: select= Select(fMatch, nbMax, "Functional")
18: RETURN select
19: ENDIF
ALGORITHM 3: SYMBOLIC REPUTATION BASED RECOMMENDATION
REQUIRE: ds: Discovered web service. S: set of all services in the UDDI
ENSURE: L: List of web services to recommend
1: L ≠∅
2: SR(ds) = get Symbolic Reputation(ds)
3: FORALL q ∈ SDO
4: VR(q) = get Vectorial Representation (q)
5: IF SR(ds)∩VR(q) ≥a THEN
6: L = L∪q
7: ENDIF
8: ENDFOR
9: L = Ranking( L)
10: L = Select( L)
11: RETURN L
7. EVALUATION
We have considered different web service sources like WebservicesX.net7
, xMethods.net8
and
seekda.com9
. Our general UDDI architecture allows using different UDDI registries, which are
used through specific wrappers. We have collected around 8,500 services. However, these
services are not classified into categories. This is a real problem when we want to evaluate the
usefulness of our representations using precision and recall measures. For this reason, we have
7
http://www.webservicex.net/ws/default.aspx
8
http://www.xmethods.net/ve2/index.po
9
http://www.seekda.com
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considered a new web services source, i.e. service-finder.eu10
, which classifies its services using
an ontology [18]. The number of services, which are classified into a category by service-finder is
only reduced to only 1,647 web services. 465 services among them are not available: they are
discarded. The collected web services are multi-languages. We have automated the recognition of
the language and keep only those using English language. Finally, 993 web services are used.
Let us denote ’Collected WS’ the number of collected web services for each category,’ Available
WS’ the number of available services and ’Used WS’ the number of used services for each
category after the process of recognition of the language. See Table 1 for more details.
Table 1. Classification of the used web services according to the category
Categories Collected WS Available WS Used WS
Weather 57 39 35
Address Information 433 316 301
SMS 45 36 20
Currency Exchange 27 23 18
Stock 163 153 142
Payment 47 23 15
Converters 23 15 9
News 37 29 25
Jobs 18 13 12
Travel 653 433 331
Logistics Shipping 14 12 6
Multimedia Video 10 10 8
Identity Verification 22 13 11
Government 11 7 7
Mathematics 36 26 24
Translation 51 34 29
Total 1647 1182 993
In the rest of this section, we discuss the results of the web services discovery considering the
different presented representations. Finally, we discuss the web services recommendation results.
We use the two classical measures, Precision and Recall, to evaluate the performance of our
approach. Precision and Recall are often used to evaluate information retrieval schemes [14].
Precision can be seen as a measure of exactness or fidelity, whereas Recall is a measure of
completeness [14]. In our context, the Precision and Recall are defined as follows:
Precision =
A
B
(3)
Recall =
A
C
(4)
Where Ais the number of relevant services retrieved, B is the total number of irrelevant and
relevant services retrieved, and C is the total number of relevant services in the whole
collection.
In order to evaluate the interest of the evoked representations, i.e., B, RBTT and SR , we have
considered the following experimental protocol :
10
http://www.service-finder.eu
15. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
15
• Discover the web services that belong to a given category. To achieve this goal, we
generate a query reqc defined by the name of category c, for example "Weather",
"Address Information", "SMS", "Currency Exchange" and "Stock".
• Compute the precision and the recall for each representation. These measures compare
the returned result with the services belonging to the category (see Table 1).
• Evaluate the global precision and recall for each representation.
The results, given in Table 2, show that the categories of web services are not represented at the
same level of precision. In general, the representation RBTT is the best. Then we have B and in
last SR. Note that the overall recall value is medium about all representations. In fact, the RBTT
is the best representation for the category ’Weather’. For the other categories, we note that only
the RBTT has obtained the medium values. Let us remark that B and SR representations give the
lowest precisions for respectively "Currency" and "Jobs"(see Table 2). Finally, we have the
category "SMS" for which we obtain medium results except for SR that gives the lowest
precisions. In conclusion, this is difficult to find an efficient representation for all categories as
the description of web services are generally heterogeneous. The lessons we have learned from
our experimental results are:
1. The traditional representation B may be efficient for some categories but is not robust and
may lead to very low precisions,
2. The symbolic reputation (SR) is not efficient for web services discovery, except for one
category (i.e. ’Weather’), and consequently it will not be used for web services discovery,
3. The rules based text tagging (RBTT) is robust (i.e. the results do not vary according to
the category) and gives correct precision results for all categories.
The previous results show that the symbolic reputation is not appropriate for web service
discovery. In fact, it does not represent the description of a service itself, but it represents the
description of the relation the service has with others. Thus, we have used the SR representation
for web services recommendation (see Algorithm 3). The basic idea behind this task is to enrich
the returned results during the web services discovery. For each discovered service, we search the
set of web services to be recommended with these discovered services. The result of a query is
not reduced only to the services that match the query, but it also contains recommended services
for each discovered service (Target service).
The recommended services are ranked using the ranking process (Algorithm 3), which uses the
computed Qos score and the reputation scores. We select only the services with the highest scores
and return them to the consumer. Table3 shows the results for services belonging to the
"Weather" category. We have evaluated manually these recommended services. The result is
really significant as the recommended services generally perform the same task, i.e. belongs to
the same category of the target service or may be composed with it.
Table 2.Web service discovery evaluation (Precision (P) and Recall (R) in %).
Categories
B RBTT SR
P % R% P% R% P% R%
Weather 68,97 57,14 81,82 51,43 68,97 57,14
SMS 48,48 80,00 55,56 50,00 12,12 80,00
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Currency Exchange 16,67 16,67 58,33 77,78 8,51 22,22
Jobs 04,35 08,33 57,14 33,33 6,67 41.67
Average 34,61 40,53 63,21 53,13 24,06 50,27
Table 3. Recommended web services for the category "Weather"
Target service:
http://www.deeptraining.com/webservices/weather.asmx?wsdl
Recommended web services:
http://ws.cdyne.com/WeatherWS/Weather.asmx?wsdl
http://ws.soatrader.com/harbormist.com/0.1/WeatherService?WSDL
http://rightactionscript.com/webservices/nusoap/server.php?wsdl
http://www.deeptraining.com/webservices/weather.asmx?wsdl
http://ws.soatrader.com/weather.gov/0.1/ndfdXML?WSDL
http://ws.soatrader.com/cis.temple.edu/0.1/DOTSFastWeather?WSDL
http://ws2.serviceobjects.net/fw/fastweather.asmx?wsdl
http://ws.serviceobjects.com/fw/FastWeather.asmx?WSDL
http://ws.soatrader.com/webservicex.com/0.2/GlobalWeather?WSDL
http://ws.soatrader.com/ejse.com/0.1/Service?WSDL
http://ws.soatrader.com/deere.com/0.1/WeatherForecastServiceService?WSDL
8. CONCLUSION
This paper revisits the traditional representation of web services using its textual descriptions and
the elements of the WSDL structure as types, attributes, etc. We have proposed two new web
services representations. The first one is based on semantic tagging of web services descriptions
to keep only the most significant parts. Whereas, the second proposed representation, i.e.,
symbolic reputation is more contextual as it does not consider only the service itself, but its
neighbours. The main conclusions of the experimentation with real-world web services are:
• Traditional representation (B) behaves correctly but it is not robust when we consider
different concepts (categories).
• Rules based text tagging (RBTT) is more robust(i.e. the results do not vary according to
the category) even if its precision is lower than traditional representations.
• Symbolic reputation (SR) is not appropriate for the web services discovery tasks but it is
more efficient for the web services recommendation task.
In the future, we will continue our work on symbolic reputation for the recommendation task. We
will increase the number of web services used for the experimentation. Finally, we will make
available online the gate corresponding to the full implementation of our system.
17. International Journal on Web Service Computing (IJWSC), Vol.4, No.1, March 2013
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