Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies.
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
A DOMAIN INDEPENDENT APPROACH FOR ONTOLOGY SEMANTIC ENRICHMENTcscpconf
Ontology automatic enrichment consists of adding automatically new concepts and/or new relations to an initial ontology built manually using a basic domain knowledge. In a concrete manner, enrichment is firstly, extracting concepts and relations from textual sources then putting them in their right emplacements in the initial ontology. However, the main issue in that process is how to preserve the coherence of the ontology after this operation. For this purpose, we consider the semantic aspect in the enrichment process by using similarity techniques between terms. Contrarily to other approaches, our approach is domain independent and the enrichment process is based on a semantic analysis. Another advantage of our approach is that it takes into account the two types of relations, taxonomic and non taxonomic
ones.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
In this paper we tried to correlate text sequences those provides common topics for semantic clues. We propose a two step method for asynchronous text mining. Step one check for the common topics in the sequences and isolates these with their timestamps. Step two takes the topic and tries to give the timestamp of the text document. After multiple repetitions of step two, we could give optimum result.
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
For #Enquiry
https://www.tutorsindia.com
info@tutorsindia.com
(Whatsapp): +91-8754446690
(UK): +44-1143520021
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
ConNeKTion: A Tool for Exploiting Conceptual Graphs Automatically Learned fro...University of Bari (Italy)
Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, keyword extraction and information retrieval. A suitable control panel is provided for the user to comfortably carry out these activities.
Scalability and availability are two highly desirable attributes pertaining to reliability of a web application that renders state-of-the-art services to online users. In simple words scalability is the ability to grow, the ability to serve increased number of requests or clients. Building a scalable application with round the clock availability is a challenging problem in the light of ever increasing population of potential users. Dramatic increase in users to web application causes bursts of requests that put the application to acid test. On the other hand web application availability represents the degree of operational continuity. High availability and unlimited scalability are the two indispensable quality attributes a web application in the real word. These features bestow rich user experience as far as operational continuity and ability to handle growing workload are concerned. By taking server side measures it is possible to achieve these two desirable features. However, there is possibility to have architectural pattern along with underlying design patterns to promote these quality features of web application. In this paper we enhance our architectural pattern eXtensible Web Application Development Framework (XWADF) that can leverage the quality of web application design as it result in highly scalable with high availability. As the application is designed in XWADF framework it promotes scalability assuming server side supports in terms of resources. The application also ensures availability as the design supports maintenance without letting the application down. The empirical results revealed that our architectural approach increases reliability of web applications significantly in terms of availability and scalability
Farthest first clustering in links reorganizationIJwest
Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure. For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used. Our aim is to perform reorganization with fewer changes in website structure.
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
A DOMAIN INDEPENDENT APPROACH FOR ONTOLOGY SEMANTIC ENRICHMENTcscpconf
Ontology automatic enrichment consists of adding automatically new concepts and/or new relations to an initial ontology built manually using a basic domain knowledge. In a concrete manner, enrichment is firstly, extracting concepts and relations from textual sources then putting them in their right emplacements in the initial ontology. However, the main issue in that process is how to preserve the coherence of the ontology after this operation. For this purpose, we consider the semantic aspect in the enrichment process by using similarity techniques between terms. Contrarily to other approaches, our approach is domain independent and the enrichment process is based on a semantic analysis. Another advantage of our approach is that it takes into account the two types of relations, taxonomic and non taxonomic
ones.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
In this paper we tried to correlate text sequences those provides common topics for semantic clues. We propose a two step method for asynchronous text mining. Step one check for the common topics in the sequences and isolates these with their timestamps. Step two takes the topic and tries to give the timestamp of the text document. After multiple repetitions of step two, we could give optimum result.
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
For #Enquiry
https://www.tutorsindia.com
info@tutorsindia.com
(Whatsapp): +91-8754446690
(UK): +44-1143520021
A Natural Logic for Artificial Intelligence, and its Risks and Benefits gerogepatton
This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists
and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be
taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
ConNeKTion: A Tool for Exploiting Conceptual Graphs Automatically Learned fro...University of Bari (Italy)
Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, keyword extraction and information retrieval. A suitable control panel is provided for the user to comfortably carry out these activities.
Scalability and availability are two highly desirable attributes pertaining to reliability of a web application that renders state-of-the-art services to online users. In simple words scalability is the ability to grow, the ability to serve increased number of requests or clients. Building a scalable application with round the clock availability is a challenging problem in the light of ever increasing population of potential users. Dramatic increase in users to web application causes bursts of requests that put the application to acid test. On the other hand web application availability represents the degree of operational continuity. High availability and unlimited scalability are the two indispensable quality attributes a web application in the real word. These features bestow rich user experience as far as operational continuity and ability to handle growing workload are concerned. By taking server side measures it is possible to achieve these two desirable features. However, there is possibility to have architectural pattern along with underlying design patterns to promote these quality features of web application. In this paper we enhance our architectural pattern eXtensible Web Application Development Framework (XWADF) that can leverage the quality of web application design as it result in highly scalable with high availability. As the application is designed in XWADF framework it promotes scalability assuming server side supports in terms of resources. The application also ensures availability as the design supports maintenance without letting the application down. The empirical results revealed that our architectural approach increases reliability of web applications significantly in terms of availability and scalability
Farthest first clustering in links reorganizationIJwest
Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure. For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used. Our aim is to perform reorganization with fewer changes in website structure.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Semantic web based software engineering by automated requirements ontology ge...IJwest
This paper presents an approach for automated generation of requirements ontology using UML diagrams in service-oriented architecture (SOA). The goal of this paper is to convenience progress of software engineering processes like software design, software reuse, service discovering and etc. The proposed method is based on a four conceptual layers. The first layer includes requirements achieved by stakeholders, the second one designs service-oriented diagrams from the data in first layer and extracts XMI codes of them. The third layer includes requirement ontology and protocol ontology to describe behavior of services and relationships between them semantically. Finally the forth layer makes standard the concepts exists in ontologies of previous layer. The generated ontology exceeds absolute domain ontology because it considers the behavior of services moreover the hierarchical relationship of them. Experimental results conducted on a set of UML4Soa diagrams in different scopes demonstrate the improvement of the proposed approach from different points of view such as: completeness of requirements ontology, automatic generation and considering SOA.
While the world is witnessing an information revolution unprecedented and great speed in the growth of databases in all aspects. Databases interconnect with their content and schema but use different elements and structures to express the same concepts and relations, which may cause semantic and structural conflicts. This paper proposes a new technique for integration the heterogeneous eXtensible Markup Language (XML) schemas, under the name XDEHD. The returned mediated schema contains all concepts and relations of the sources without duplication. Detailed technique divides into three steps; First, extract all subschemas from the sources by decompose the schemas sources, each subschema contains three levels, these levels are ancestor, root and leaf. Thereafter, second, the technique matches and compares the subschemas and return the related candidate subschemas, semantic closeness function is implemented to measures the degree how similar the concepts of subschemas are modelled in the sources. Finally, create the medicate schema by integration the candidate subschemas, and then obtain the minimal and complete unified schema, association strength function is developed to compute closely of pair in candidate subschema across all data sources, and elements repetition function is employed to calculate how many times each element repeated between the candidate subschema.
Implementing High Grade Security in Cloud Application using Multifactor Auth...IJwest
As a high
-
speed internet foundation is being developed and people are informationized, most
of the tasks are engaged in internet field so there is
a risk that any private data like personal information or
applications for managing money can be wiretapped or eavesdropped. The consolidation of One Time
Passwords (OTPs) and Hash encryption algorithms are used to evolve a more secured password
-
protected
web sites and data storage systems. The new outlined scheme had higher security, small system overhead
and is easy to implement.
LOANONT-A RULE BASED ONTOLOGY FOR PERSONAL LOAN ELIGIBILITY EVALUATIONIJwest
In recent years, significant attention has been given to understand and implement banking solutions. The
global competitive business environment and advancement in Information Technology and in particular
internet technologies has facilitated the carrying out of banking activities outside the brick and mortar
premise of the banks. Credit availing schemes are the core of the banking industry. Many agencies are
working on it so as to make this facility hassle free for the customers and also to minimize the losses
incurred by the banks in the form of bad debts. The challenge has been, and still is, to recognize,
communicate and steadily improvise the banking solutions. The internet technologies are a potential
candidates to overcome these challenges. The paper describes LoanOnt Ontology with the associated
implementation toolset for creating an interoperable and sustainable personal loan calculation solution
which would provide an intercommunication platform to facilitate integration and interoperation of
information across interacting applications in banking scenarios.
Ontology-Oriented Inference-Based Learning Content Management System dannyijwest
The world is witnessing the electronic revolution in many fields of life such as health, education,
government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings
with it rapid change and greater opportunities to increase learning ability in colleges and schools. The
fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are
full of open source and commercial products, however LCMS systems in general inherit the drawbacks of
information system such as weakness in user expected information retrieval and semantic modelling and
searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the
Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the
constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an
organized fashion. This enables construction of a user-specific course, by semantic querying for topics of
interest. We present the development of an Ontology-oriented Inference-based Learning Content
Management System OILCMS, its architecture, conception and strengths.
Ontology-Oriented Inference-Based Learning Content Management System dannyijwest
The world is witnessing the electronic revolution in many fields of life such as health, education,
government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings
with it rapid change and greater opportunities to increase learning ability in colleges and schools. The
fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are
full of open source and commercial products, however LCMS systems in general inherit the drawbacks of
information system such as weakness in user expected information retrieval and semantic modelling and
searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the
Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the
constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an
organized fashion. This enables construction of a user-specific course, by semantic querying for topics of
interest. We present the development of an Ontology-oriented Inference-based Learning Content
Management System OILCMS, its architecture, conception and strengths.
The world is witnessing the electronic revolution in many fields of life such as health, education, government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings with it rapid change and greater opportunities to increase learning ability in colleges and schools. The fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are full of open source and commercial products, however LCMS systems in general inherit the drawbacks of information system such as weakness in user expected information retrieval and semantic modelling and searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an organized fashion. This enables construction of a user-specific course, by semantic querying for topics of interest. We present the development of an Ontology-oriented Inference-based Learning Content Management System OILCMS, its architecture, conception and strengths.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
An Approach to Owl Concept Extraction and Integration Across Multiple Ontolog...dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for
the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be
applied for making the process of ontology development through reuse a viable option for ontology
developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are
reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic
Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on
cost as well as development life as ontology engineering requires expert domain skills and is time taking
process. We have devised a framework to address challenges associated with reusing ontologies from the
Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across
multiple ontologies.
Building an Ontology in Educational Domain Case Study for the University of P...IJRES Journal
The current web is based on HTML which cannot be demoralized by information retrieval techniques and therefore processing of information on the web is generally restricted to manual keyword searches which results in unrelated information retrieval, so the semantic web was founded to resolve this problem; furthermore, ontology is used to capture knowledge about any domain of interest with the goal of integrating the machine understandable data on the current human-readable web. Web Ontology Language (OWL) is a semantic markup language for sharing ontologies on the web. In this paper, the education domain and the development of a University Ontology using Protégé 4.1 Editor is considered. The University of Palestine was chosen as an example for the Ontology Development and the diverse aspects: super class and sub class hierarchy, creating a sub class, instances for classes illustration, query retrieval process using the Unified Process for Building the Ontology (UPON) technique.
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
A Comparative Study Ontology Building Tools for Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d) market status and penetration. The results of the review in ontologies are analyzed for each application area, such as transport, tourism, personal services, health and social services, natural languages and other HCI-related domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks. Although each tool provides different functionalities, most of the users just use only one, because they are not able to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different ontologies with different development and management tools. The paper is also concerns the detection of commonalities and differences between the examined ontologies, both on the same domain (application area) and among different domains.
A Comparative Study of Ontology building Tools in Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
Concept integration using edit distance and n gram match ijdms
Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all
this information not only available to people but also to the machines. Ontology and token are widely being
used to add the semantics in data processing or information processing. A concept formally refers to the
meaning of the specification which is encoded in a logic-based language, explicit means concepts,
properties that specification is machine readable and also a conceptualization model how people think
about things of a particular subject area. In modern scenario more ontologies has been developed on
various different topics, results in an increased heterogeneity of entities among the ontologies. The concept
integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data
processing. There are various techniques to integrate the concepts from different input sources, based on
the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept
(Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept
frequency is used to dominate the integration process. The proposed techniques performance is compared
with semantic similarity based integration techniques on quality parameters like Recall, Precision, FMeasure
& integration efficiency over the different size of concepts. The analysis indicates that edit
distance value based interaction outperformed n-gram integration and semantic similarity techniques.
A Review on Evolution and Versioning of Ontology Based Information Systemsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEWijait
Protégé is one of the most popular tools of the ontology visualization. The “Protégé” tools are being applied for further development in various disciplines for better understanding of knowledge. These tools commonly use four methods of ontology visualization, namely, indented list, node-link and tree, zoomable, and focus+context. The purpose of this work is to present a study on application of these four methods in the development of different kinds of protégé visualization tools and categorize their characteristics and features so that it assists in method selection and promotes further future research in the area of ontology visualization.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW ijait
Protégé is one of the most popular tools of the ontology visualization. The “Protégé” tools are being applied for further development in various disciplines for better understanding of knowledge. These tools commonly use four methods of ontology visualization, namely, indented list, node-link and tree,
zoomable, and focus+context. The purpose of this work is to present a study on application of these four methods in the development of different kinds of protégé visualization tools and categorize their characteristics and features so that it assists in method selection and promotes further future research in
the area of ontology visualization.
Similar to Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network (20)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Literature Review Basics and Understanding Reference Management.pptx
Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network
1. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
DOI : 10.5121/ijwest.2014.5403 33
Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network
K.Karthikeyan1 and Dr.V.Karthikeyani2
1Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu
2Department of Computer Science, Thiruvalluvar Govt. Arts College, Rasipuram, TamilNadu
ABSTRACT
Ontology may be a conceptualization of a website into a human understandable, however machine- readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
KEYWORDS
Ontology, Semantic Web, Markov Logic Network, Semantic Relation, Association Rule Mining.
1. INTRODUCTION
Ontology is a specification of conceptualization it specifies the meanings of the symbols in an information systems. This ontology has three kinds of components like individuals, classes and properties. Individuals are nothing but the things or objects in the world. Classes are a set of individuals. Properties are between individual and their values. Ontology learning things about with information discovery numerous knowledge [4] sources associated with its illustration through an onto logic structure and together with metaphysics population, constitutes associate approach for automating the information acquisition method. Ontology may be an illustration of information formal. It offer a transparent and consistent illustration of nomenclature and ways that facilitate individuals to watch issues and managing affairs, offer public vocabulary of areas and outline totally different levels of formal meanings of terms and relationship between terms.
Ontologies square measure the backbone of the linguistics internet also as of a growing range of knowledge-based systems. A long standing goal of Artificial Intelligence is to create an autonomous agent which will scan and understand text. The method of meta-physics learning from text includes three core subtasks. There are learning of the ideas which will represent the meta-physics, learning of the linguistics relations among these ideas and at last one is learning of
2. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
34
a set of abstract through rules. Ontology learning tools discover binary relations not just for lexical things however additionally for ontological ideas. Ontology learning is bothered with data acquisition and within the context of this volume additional specifically with data acquisition from text. Ontology learning is inherently multi-disciplinary attributable to its robust affiliation with the semantic web.
In ontology learning [9], [10], [11], [26] had two kinds of evaluation procedures. There are manual evaluation and posteriori evaluation. Manual evaluation has an advantage which is supposed to know the concepts and their relationships in their domain of expertise, they are supposedly able to tell either a given ontology represents a domain or not. The manual evaluation is subjective and time consuming. The posteriori evaluation also same like manual evaluation but small change in that it is more time consuming and represent the evaluation properly. The grammatical relations are arranged in a hierarchy, rooted with the most generic relation, dependent. When the relation between a head and its dependent can be identified more precisely, relations further down in the hierarchy can be used.
Ontologies became omnipresent in current generation information systems. Associate meta- physics is associate explicit, formal illustration of the entities and relationships which will exist during a domain of application. The term meta-physics came from philosophy and was applied to data systems within it have to characterize the formalization of a body of information describing a given domain. One amongst the key drivers for the popularity of that idea was the conclusion that many of the foremost difficult issues within the data technology field, cannot be resolved while not considering the linguistics intrinsically embedded in every explicit data system.
Ontology learning uses [11] methods from a diverse spectrum of fields such as machine learning, knowledge acquisition, natural language processing, information retrieval, artificial intelligence, reasoning, and database management. Ontology learning framework for the Semantic Web that included ontology importation, extraction, pruning, refinement, and evaluation giving the ontology engineers a wealth of coordinated tools for ontology modeling.
Semantic ability [22] means that not simply that two distinct knowledge systems are ready to exchange information during a given format, that the token should have an equivalent which means for each parties. Customary illustration languages for ontologies like the raptorial bird net meta-physics language and publicly obtainable ontologies will greatly facilitate the event of practical systems, but the process of desegregation and reusing ontologies remains fraught with issue. Strategies for machine-controlled discovery of information and construction of ontologies will facilitate to beat the tedium and mitigate non-compliance however gaps and inconsistencies are inescapable.
Learning semantic [6] resources from text instead of manually creating them might be dangerous in terms of correctness, but has undeniable advantages: Creating resources for text processing from the texts to be processed will fit the semantic component neatly and directly to them, which will never be possible with general-purpose resources. Further, the cost per entry is greatly reduced, giving rise to much larger resources than an advocate of a manual approach could ever afford.
Semantic annotation[19] of a corpus will be performed semi-automatically by varied annotation tools that speed up the entire procedure by providing a friendly interface to a website knowledgeable. A manually annotated corpus will be wont to train associate degree info extraction system. The aim of those approaches is that the exploitation of associate degree initial small-sized lexicon and a machine learning-based system for the lexicon enrichment through associate degree repetitive approach.
3. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
35
Producing robust components to process human language as part of applications software requires attention to the engineering aspects of their construction. For that purpose we have to use the GATE (General Architecture of Text Engineering) tool. This tool [27] is used to perform the dataset progress procedure. According to this tool we had to load our xml dataset. This could be processed to generate the input files for further processing. This GTAE tool shall be used in initial stage of ontology process. The ontology learning process mainly focused this kind of tool for enlarge the ontology process. The framework provides a reusable design for an LE software system and a set of prefabricated software building blocks. As a development process it helps its users to minimize the time they spend building new LE systems or modifying existing ones, by aiding overall development and providing a debugging mechanism for new modules.
The GATE framework contains a core library and a set of reusable lupus modules. The framework implements the design and provides facilities for process and visualizing resources, together with illustration, import and export of information. GATE element is also enforced by a spread of programming languages and databases, however in every case they are delineate to the system as a Java category.
Statistical Relative Learning (SRL) focuses [2] on domains wherever knowledge points square measure not freelance and identically distributed. It combines ideas from applied mathematics learning and inductive logic programming and interest in its adult rapidly. One in every of the foremost powerful representations for SRL is Markov logic, that generalizes each markov logic random fields and first-order logic.
Weight Learning in Markov logic could be a bell-shaped improvement drawback, and therefore gradient descent is absolute to realize the worldwide optimum. However, convergence to the present optimum is also very slow. MLN’s [3] square measure exponential models, and their decent statistics square measure the number of times every clause is true within the information. The Markov Logic random fields [12] computing the chance in MLNs needs computing the partition functions, that is mostly stubborn. This makes it troublesome to use ways that require activity line searches, that involve computing the perform as well as gradient.
Wordnet was accustomed notice nouns that area unit derived from verbs and to filtrate words that are not in the noun information. Noun in wordnet area unit joined to their derivationally connected verbs, however there is no indication regarding that springs from that. WordNet, that uses documents retrieved from the Web. However, in their approach, the query strategy is not entirely satisfactory in retrieving relevant documents which affects the quality and performance of the topic signatures and clusters. Using Word Net to enrich vocabulary for ontology domain, we have presented the lexical expansion from Wordnet approach providing a method of accurately extract new vocabulary for an ontology for any domain covered by WordNet.
The vocabulary of associate degree object language for a given domain consists of names representing the people of the domain, predicates standing for properties and relations and of logical constants.
The meaning of a predicate is in general not independent of the meaning of other predicates. This interdependence is expressed by axioms and intensional and extensional definitions. An extensional definition of a predicate is simply the list of the names of the individuals that constitute its extension. An intensional definition of a predicate (definiendum) is the conjunction of atomic sentences (definientia) stating the properties an individual must possess for the predicate to apply. Ontology for an object language is a non-logical vocabulary supplemented by a set of
4. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
36
axioms
intensional definitions
extensional definitions
The axioms picture structural properties of the domain and limit the possible interpretation of the primary terms. The intensional and extensional definitions are terminological.
There are containing two ways that conceiving ontology construction, very cheap up predominant within the methodology of arithmetic and a prime down approach that’s predominant disciplines wherever the domain consists of objects of the planet a given as in science.
Alchemy is a software package [13] providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy may be a computer code tool designed for a large varies of users. It assumes the reader has noises of classical machine learning algorithms and tasks and is accustomed to first-order and markov logic and a few likelihood theory. Alchemy may be a add progress and is frequently being extended to satisfy these desires.
During weight learning every formulas [15] are reborn to conjunctive traditional form (CNF) and a weight is learned for every of its clauses. If a formula is preceded by a weight within the inpu.mln file, the load is split equally among the formula’s clauses. The load of a clause is employed because the mean of a Gaussian previous for the learned weight.
This paper [8] told Anaphora resolution system. This anaphora lets be the front step of Natural Language Processing (NLP). Understanding the Text means, understanding the meaning of context or concept. Anaphora system produces most likelihood antecedent after development of machine learning approach. The knowledge poor strategy provides best results compare to previous knowledge rich strategy. The computational strategy provide maximum share to produce most accurate antecedent. But not least, preprocess task is base for computational strategy perform well good manner.
The name of animal or human being is more concentrate in AR community to categorization of candidate set. It is easy to accept or reject the discourse in candidate set. So, we concentrate much effort to be taken against to recognize the animacy agreement. But this system constructed rule based AR system.
Learning from text and natural language is one of the great challenges of Artificial Intelligence and machine learning. The Probabilistic Latent Semantic Analysis (PLSA) [7] is a statistical model which has been called as Aspect Model. The aspect model is a latent variable model for co-occurrence data which associate an unobserved class variable. The PLSA model has an advantage of the well established statistical theory for model selection and complexity control to determine the optimal number of latent space dimensions.
Commonsense knowledge parsing can be performed using a combination of syntax and semantics, via syntax alone (making use of phrase structure grammars), or statistically, using classifiers based on training algorithms. Probabilistic inference aims at determining the probability of a formula given a set of constants and, maybe, other formulas as evidence. The probability of a formula is the sum of the probabilities of the worlds where it holds.
Ontology learning can be dividing the portion of relationship mechanism. This process called as semantic relation extraction. This kind of ontology learning process usually takes the steps of finding weight learning. The first process behind in this stage is to construct the predicates and
5. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
37
first order logic formula. A first order logic formula can be constructing from the first order logic knowledge base using Markov Logic Network. According to the predicates we have to build the MLN file. After that build the training file and also the test files. These all based on the input we are taking at the time initialization. Here, we have to find the inference values and last predicated number etc., and then we have to learn the parameter weights of each and every document progressed in our corpus.
As a summary, the main contributions of our work include the following: i. We present the novel methodology of Ontology Based Semantic Relation Extraction to find the frequent items relationship based on association rule mining. ii. We present the construction of predicates and first order logic and weight learning processes based on Markov Logic Networks. iii. Then we present the avoidance of non useful words and also redundant words and make the tree structure using thesaurus.
The rest of this paper is organized as follows: Section 2 reviews the related work. Section 3 contains the process of General Markov Logic Network details. Section 4 formulates the proposed framework of Association Rule Mining Based Semantic Relation Extraction. In further section it will be called as ARSRE, gives results of the semantic relation extraction. Section 5 has the experimental results for the projects. Section 6 formulates the Conclusion for the projects and Section 7 contains the Future enhancement of the projects. Thus the paper clearly explains the concept of the ARSRE.
2. RELATED WORK
2.1 Ontology Learning
Ontology learning will be outlined because the set of methods and techniques used for building metaphysics from scratch, enriching or adapting associate in nursing existing meta-physics during semi-automatic fashion victimization many sources. Ontology may [10], [26] be an illustration of information formal. It offer a transparent and consistent illustration of nomenclature and ways that facilitate individuals to watch issues and managing affairs, offer public vocabulary of areas and outline totally different levels of formal meanings of terms and relationship between terms. The meta-physics will be considered as vocabulary of terms and relationships between those terms during a given domain. Ontology learning use strategies from a various spectrum of fields like machine learning, information acquisition, natural language process, information retrieval, computer science, reasoning and database management.
2.2 Semantic Web
Semantic internet technologies are aimed toward providing the mandatory representation languages and tools to bring linguistics to the current internet contents. The semantic Meta data [5], [16] can be producing the language of Resource Description language. The RDF we are having three kinds of elements. There are resources, literals and properties. The resources are also called as web objects. The web objects are identified through URI. Literals are the atomic values it contain the values of strings, dates and numbers. Properties also identified through URI. Properties are the binary relationship between resources and literals. The linguistics internet offers the potential for facilitate, permitting a lot of intelligent search queries supported websites marked up with linguistics data.
6. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
38
2.3 Semantic Relation
Semantic relation [6] it might be potential to extract the whole family-tree of an outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics a relationship among the entities involves that is beneficial for a higher understanding of human language. Semantic relations square measure a vital component within the construction of ontologies and models of downside domains.
To express the relation we are attempting to accumulate information regarding lexical and alternative linguistics relations aside from similarity or synonymy by distinctive syntactical constructions. This paper [28], [14] presents discovering the semantic relations between the ontology concepts. Here this could be divided into two kinds of concepts. There high level concepts and low level concepts. The discovery process isn’t supported the belief that verbs typically indicate semantic relation between ideas and doesn’t exploit lexicon-syntactic patterns or cluster ways or any external data sources like wordnet. Discovering semantic relation between high level concepts based on annotation. In this approach, for each document we search for the different pairs of concepts that have overlapping instances. We assume that abstraction overlap between conception instances within the text implies a semantic relationship. In final part of in this high level concepts we simply apply applied math methods to document information that’s to the placement of conception instances in text. To discover the semantic relation [19] between low-level concepts we have to apply the variation on the definition of the instance offset of each low level concept. Using a named entity recognizer for the annotation of low-level ideas instances we will be ready to any automize the applying of the projected variation to get linguistics relations between low-level ideas. Semantic relation [29] it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics a relationship among the entities involves that is beneficial for a higher understanding of human language. Semantic relations square measure a vital component within the construction of ontologies and models of downside domains.
2.4 Association Rule Mining
Association rules [18] square measure supported the notion of group action that is an observation of the co-occurrence of a collection of things. Once handling southwest information, the most challenge consists in distinguishing interesting transactions and things from the semi-structured and heterogeneous nature of that information. During this means, it becomes crucial to use the maximum amount as potential the data provide by the ontologies so transactions will be simply mined. The basic task of association data processing is to seek out all association rules with support and confidence bigger than user fixed minimum support and minimum confidence threshold values. Association rule mining is taken into account jointly of the foremost vital tasks in data discovery in databases. Association rule mining employs the support live, that treats dealings equally. In contrast, totally different transactions have different weights in datasets. Typically association rule square measure generated ranging from frequent item sets generated from frequent item sets. The generalized association rule learning algorithmic program extends its baseline by aiming at descriptions at the appropriate taxonomy level.
2.5 Syntactic Structure Features
A Syntactic Analysis [20] is useful because, it provides a hierarchical set of groupings words and phrases which can be the basis for a general-purpose, finite and compositional procedure to extract meaning from any sentence of the language. A syntactic analysis tells us that phrases. It could be argued that it is impossible to talk of a natural grouping without considering meaning. Syntactic structures[24] that reflect these groupings of items into larger meaningful units. This
7. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
39
idea of compositional semantics is very widespread, and it is one of the guidelines which will be adopted here in deciding on suitable syntactic structures. Establishing the grammar structure of resultatives, we tend to return to a more general conclusion regarding the connection between the two levels of grammatical representation. It is necessary to grasp that the mnemotechnic names given to those classes area unit basically whimsical, because it is that the approach that the labels area unit employed in the principles and within the lexicon that offers significance to them. Syntactic approaches use similarity considering the relationships between predicate and argument.
2.6 First-Order Logic
A first order knowledge base [15] could be a set of sentences or formulas in first-order logic. Formulas area unit created victimization four kinds of symbols. There are constants, variables, functions and predicates. Constant symbols represent objects within the domain of interest. Variable symbols vary over the objects within the domain. Operate symbols represent mappings from tuples of objects to things. Predicate symbols represent relations among objects within the domain or attributes of objects. A first-order computer memory unit is often seen as worlds, if a world violates even one formula, its zero likelihood. The basic idea in markov logic is to melt these constraints. Once a world violates one formula within the computer memory unit it’s less probable, however not possible.
Our work is also related with some existing support of predicates based markov logic network. This process inputs are taken comes under the progress of concept hierarchy extraction. This could have the results of web based information of every document. The performance of this work related to the semantic relation. The authors [15] explore the issues of semantic relation extraction. So, our goal is to improve the semantic relation extraction process based on Markov Logic Networks. To employ our process, before performing the semantic relation extraction we do some progress of construction of predicate and first-order logic formula. After that we perform the process of finding inference and weight learning. For finding the inference we use Markov Logic Network. For weight learning we can use the formulas and also database of atoms for calculates the weights. Learning rate can be used to perform this kind of task.
As a final point, there are some works adopt to improve the semantic relation extraction. Association rule mining mainly used to find the relationship between the frequent item sets. Next, conversion process is to the extensibility of existing lightweight ontologies to formal one. These processes are performing by the proposed techniques of named as ARSRE. This method that provides the effective and efficient results.
3. MARKOV LOGIC NETWORK
Markov Logic [17] is indistinguishable from first-order logic, except that every formula includes a weight connected. Markov logic may be a straightforward nonetheless powerful combination of markov networks and first-order logic. Markov logic raises the quality of markov networks to encompass first-order logic. Recall that a first-order domain is defined by a collection of constants representing objects within the domain and a collection of predicates representing properties of these objects and relations between them. A predicate are often grounded by replacement its variables with constants. A first-order Knowledge Base (KB) may be a set of formulas in first-order logic, created from predicates victimization logical connectives and quantifiers. A formula in markov logic may be a formula in first-order logic with associate degree associated weight. The basic plan in markov logic is to melt these constraints. When a world violates one formula within the K it’s less probable, however not possible. Less formulas a world
8. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
40
violates, a lot of probable it’s. Associate degree MLN [1] is often viewed as a template for constructing markov networks. In different worlds it will manufacture different networks and this also has wide varied size, however all can have certain regularities in structure and parameters, given by the MLN.
Combining chance and first-order logic in a very single illustration has long been a goal of Artificial Intelligence. Probabilistic graphical models change us to efficiently handle uncertainty. First-order logic permits us to succinctly represent a good variety of information.
A Markov Logic network [21] may be a first-order knowledge domain with a weight attached to every formula, and may be viewed as a temple for constructing Markov Networks. MLNs give a compact language to specify terribly giant a Markov Networks, and therefore the ability to flexibly and modularly incorporate a large vary of domain information into them. Several necessary tasks in applied mathematics relative learning, like collective classification, link prediction, link-based cluster, social network modeling, and object identification, area unit naturally developed as instances of MLN learning and illation.
Markov Logic, linguistics internet languages can be created probabilistic just by adding weights to statements and linguistics web illation engines may be extended to perform probabilistic reasoning merely by passing the proof of Directed Acyclic Graph (DAG), with weights connected, to a probabilistic illation system. Weights is also set by hand inferred varied sources or learned mechanically from information. MLN acts as a template for a markov network. We have extended and adapted several of these standard methods to take particular advantage of the logical structure in a markov logic network, yielding tremendous savings in memory and time. Markov logic combines first-order logic and probabilistic graphical models in a unifying representation. The main idea behind Markov Logic is that, unlike first-order logic, a world that violates a formula is not invalid, but only less probable.
Statistical Relative learning combines [2] the communicatory power of data representation formalisms with probabilistic learning approaches, therefore enabling one to represent grammar dependencies between words and capturing applied mathematics information of words in text. The markov logic network represent an approach for applied mathematics relative learning that mixes first order logic with markov random fields. Associate MLN could be a first order logic mental object weights, which may be either positive or negative, associated to every formula.
The main idea behind Markov logic [23] is that, unlike first-order logic, a world that violates a formula is not invalid, but only less probable. This is done by attaching weights to first-order logic formulas: the higher the weight, the bigger is the difference between a world that satisfies the formula and one that does not, other things been equal.
Two common inference tasks in Markov Logic are the maximum a posteriori (MAP) and probabilistic inference. MAP inference aims at finding the most probable state of the world given some evidence. In Markov Logic this task is the same as finding the truth assignment that maximizes the sum of the weights of satisfied formulas. This can be done by any weighted satisfiability solver. Probabilistic inference aims at determining the probability of a formula given a set of constants and, maybe, other formulas as evidence. The probability of a formula is the sum of the probabilities of the worlds where it holds. There are two approaches for learning the weights of a given set of formulas. There are generative and discriminative learning. Generative learning aims at maximizing the joint likelihood of all predicates while discriminative, at maximizing the conditional likelihood of the query predicates given the evidence ones. Maximum-likelihood or MAP estimates of Markov network weights cannot be computed in closed form, but, because the log-likelihood is a concave function of the weights, they can be found efficiently using standard gradient based or quasi-Newton optimization methods
9. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
41
A term is any expression representing an object in the domain. It can be a constant, a variable, or
a function applied to a tuple of terms. For example, Anna, x, and GreatestCommonDivisor(x, y)
are terms. An atomic formula or atom is a predicate symbol applied to a tuple of terms (e.g.,
Friends(x, Mother of (Anna))).
Parentheses may be used to enforce precedence. A positive literal is an atomic formula; a
negative literal is a negated atomic formula. The formulas in a KB are implicitly conjoined, and
thus a KB can be viewed as a single large formula. A ground term is a term containing no
variables. A ground atom or ground predicate is an atomic formula all of whose arguments are
ground terms. A possible world or her brand interpretation assigns a truth value to each possible
ground atom.
The syntax of the formulas in an MLN is the standard syntax of first-order logic. An MLN can be
viewed as a template for constructing Markov networks. Given different sets of constants, it will
produce different networks, and these may be of widely varying size, but all will have certain
regularities in structure and parameters, given by the MLN. The Probability distribution over
possible worlds x specified by the ground markov network. It can be represented by
We defined MLNs as log-linear models, they could equally well be defined as products of
potential functions, as the second equality above shows. This will be the most convenient
approach in domains with a mixture of hard and soft constraints. The graphical structure of
markov network follows from there is an edge between two nodes of markov network iff the
corresponding ground atoms appear together in at least one grounding of one formula in L. Thus,
the atoms in each ground formula form a (not necessarily maximal) clique in markov network.
Figure 1 Ground Markov Network
A first-order KB can be seen as a set of hard constraints on the set of possible worlds, if a world
violates even one formula, it has zero probability. The basic idea in MLNs is to soften these
constraints: when a world violates one formula in the KB it is less probable, but not impossible.
The fewer formulas a world violates, the more probable it is. Each formula has an associated
weight that reflects how strong a constraint it is the higher the weight, the greater the difference in
log probability between a world that satisfies the formula and one that does not, other things
being equal. In an MLN, the derivative of the negative conditional log-likelihood (CLL)
with respect to a weight is the difference of the expected number of true groundings of the
corresponding clause and the actual number according to the data.
This last assumption allows us to replace functions by their values when grounding formulas.
Thus the only ground atoms that need to be considered are those having constants as arguments.
(1)
10. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
42
Features can also be learned from data, for example by greedily constructing conjunctions of atomic features.
4. ARSRE (ASSOCIATION RULE MINING BASED SEMANTIC RELATION EXTRACTION)
ARSRE is an Association Rule Mining Based Semantic Relation Extraction. The ARSRE describes the process of semantic relation extraction. Semantic relation is the process that contains sub process of construction of predicates and first order logic formula, Finding Inference and weight. The most existing technique of semantic relation extraction was developed from syntactic word features and markov logic networks only. In our proposed system semantic relation extraction based on finding frequent items relationship and avoidance of non useful words. Frequent items should be found with the help of association rule mining. The ARSRE techniques have some types. There are Construction of first order logic predicates, finding inference and weigh, semantic relation extraction and also avoidance of non useful words. The OBCHED process clearly explains in figure 2.
4.1 Construction of First Order Logic Predicates
Here, we are going to handle the process of first order logic predicates. For that purpose we have to take the methods of two kinds. One is selection of predicates based on word features. This word features contains the process of extraction of domain concepts, instances, attributes, values an also the relationships. This kind of semantic relation extraction process we choose the choice of features to consider the word features and punctuation features, such as “The elevation of Fuxian Lake is 1722.5 meters", which the word “elevation" means
“Fuxian Lake" and “1722.5 meters" is the R (I, V) and the punctuation mark “:" can also reflect the R(C,I). For finding the predicates based on word features we have to use the formula like
HasWord (+w, s) ^ HasEntity (+e1, s) ^ HasEntity (+e2, s) =>HasRelation (+r, e1, e2)
Second one is selection of predicates based on syntactic features. In one sentence we are having more than three entities at that time we do not take the word feature selection it’s automatically move onto the syntactic feature selection. For describing the syntactic feature selection we can the example of “Yunnan ethnic village and Daguanlou lie between water, covers an area of 89 hectares", it has two entity “Yunnan ethnic village", “Daguanlou", and a value entity “89 hectares", the characteristic “area" is described in “Yunnan ethnic village" or “Daguanlou", during the entity relationship matching, it is easy to make mistake. For avoiding this kind of mistake we generate the formula such as,
BigThan (Distance (e1, e3), Distance (e2, e3)) ^ HasWord (+w, s) =>HasRelation (+r, e1,e3)
Here, the entity of e1 and e2 has the parallel relationship. According to this relationship we have to find the first order logic predicates. So we can express the formula like
Parallel (e1, e2) ^ HasRelation (r, e1, e3) => HasRelation(r, e2, e3)
The entity 1 and entity 2 has the relationship of parallel. According to this kind of processes we have to construct the first order predicates.
4.2 Inference and Weight Learning
The process of finding inference can be done with the help of markov logic network. The MLNs inference including maximum likelihood inference calculates the marginal probabilities and
11. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
43
calculates the conditional probability. Maximum likelihood reasoning process can be expressed a
predicate formula x and find the relative formula y which can be express the probability as
Then according to the markov logic network’s joint probability formula it can be transformed to
According to this transformation we have to find the inference value for each every document in
the corpus.
Next, we do the process of weight learning. For finding weight learning we have to take the
discriminative learning based MLN method. Then we assume the evidence predicate x and query
predicate y, this could be the large collections of y produce the probability condition of,
Here, we take the greater than true value number. According to that find the weights in the
learning process.
Originally proposed learning weights generatively using pseudo-likelihood. Pseudo-likelihood is
the product of the conditional likelihood of each variable given the values of its neighbors in the
data. While efficient for learning, it can give poor results when long chains of inference are
required at query time. Pseudo-likelihood is consistently outperformed by discriminative training,
which minimizes the negative conditional likelihood of the query predicates given the evidence
ones.
In an MLN, the derivative of the negative conditional log-likelihood (CLL) with respect to a
weight is the difference of the expected number of true groundings of the corresponding clause
and the actual number according to the data. Use this approach and discuss how the resulting
method can be viewed as approximately optimizing log-likelihood. However, the use of
discriminative MLNs is potentially complicated by the fact that the MPE state may no longer be
unique.
(2)
(3)
(4)
12. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
44
Figure 2. ARSRE Process
4.3 Association Rule Mining
The Association rule mining process is one of the contributions of our proposed system work. This association rule mining is used to find the relationship between the frequent items. Here, we take the input from semantic web data. The problem of discovering association rules can be formally described as follows. Let I = {i1, i2. . . im} be a set of m literals, called items. Let D = {t1, t2. . . tn} be a database of n transactions where each transaction is a subset of I. An itemset is a subset of items. The support of an itemset S, denoted by sup(X), is the percentage of transactions in the database D that contain S. An itemset is called frequent if its support is greater than or equal to a user specified threshold value. An association rule r is a rule of the form X=>Y where both X and Y are non-empty subsets of I and X ∩ Y. X is the antecedent of r and Y is called its consequent. The support and confidence of the association rule r: X =>Y are denoted by sup(r) and conf(r). These measures can be interpreted in terms of estimated probabilities as sup(r) = P(X ∩ Y) and conf(r) = P(Y|X) respectively. Another interesting measure is the lift or interest factor of a rule, denoted by lift(r), which computes the ratio between the rule probability and the joint probability of X and Y assuming they are independent, that is, P(X [∩Y)/(P(X) XP(Y)). If this value is 1, then X and Y are independent. The higher this value, the more likely that the existence of X and Y together in a transaction is not just a random occurrence, but because of some relationship between them.
The basic task of association data mining is to find all association rules with support and confidence greater than user specified minimum support and minimum confidence threshold values. Mining association rules basically consists of two phases. One is compute the frequent itemsets with respect to the minimum support, and another one is three kinds of elements. There are resources, literals, and properties. Resources are web objects (entities) that are identified through a URI, literals are atomic values such as strings, dates, numbers, etc., and properties are binary relationships between resources and literals. Properties are also identified through URIs. The basic building block of RDF is the triple: a binary relationship between two resources or between a resource and a literal. The resulting metadata can be seen as a graph where nodes are resources and literals, and edges are properties connecting them. RDFS extends RDF by allowing triples to be defined over classes and properties. In this way, we can describe the schema that rules our metadata within the same description framework. Later on, the ontology web language
13. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
45
(OWL) was proposed to provide well-founded logic inferences over semantic descriptions. Most sublanguages of OWL are supported by decidable subsets of the first order logic, which are known as description logics (DLs). Additionally, OWL syntax relies on RDF so that technology developed for RDF like triple stores and query languages can be directly applied to OWL. In this work we mainly focus on OWL-DL semantic descriptions. This kind of progress may be done with the help of Apriori algorithm. This apriori algorithm can be found the frequent item set of each and every document in the corpus.
4.4 Removal of Non Useful Words
In this process we have to do the process of avoidance of non-useful words. For this process we have to take the contribution of extensibility of existing lightweight ontologies into formal ontology. Ontology can be thought of as directed graphs consisting of concepts as nodes and relations as the edges between the nodes. A concept is essentially a mental symbol often realized by a corresponding lexical representation (i.e., natural language name). For instance, the concept “food” denotes the set of all substances that can be consumed for nutrition or pleasure. In Information Science, an ontology is a “formal, explicit specification of a shared conceptualisation”. This definition imposes the requirement that the names of concepts and how the concepts are related to one another have to be explicitly expressed and represented using formal languages, such as Web Ontology Language (OWL). An important benefit of a formal representation is the ability to specify axioms for reasoning in order to determine validity and to define constraints in ontologies. Moreover, a formal ontology is natural language independent or in other words, does not contain lexical knowledge.
The output of our process is terms in a controlled vocabulary should be defined in a way to minimize or avoid ambiguity and redundancy. A taxonomy, on the other hand, is a controlled vocabulary organized into a hierarchical or parent-child structure. A thesaurus is similar to a taxonomy, with the addition of more relationships beyond hierarchical.
Here, we have the non-useful words and also the redundant words in our selected semantic web data document. After removing these words the structure of tree can be represented in our output window. It can be shown the complete process results of each and every document in our corpus. Because here, we were chosen the corpus of lonely planet. This lonely planet can contain the details of tourism.
5. EXPERIMENTS
In this allotment, we achieve the inclusive set of experiments to examine the performance of the proposed method of ARSRE framework by comparing it into the state-of-art method. The proposed ARSRE technique consists of three processes. There are Association Rule Mining, Removal of Redundant Words and Learning process. This technique can provide improved results of semantic relation extraction. Using this proposed technique we have to avoid the redundant and also non useful words in progress. The semantic relation extraction results and some other dataset results are should be given below.
5.1 Experimental Testbed
In our experimental testbed we have to use the input of lonely corpus planet dataset. This dataset having the fields of countries, cities, cultures, organization, person and etc. This dataset is taken fromthe http://www.lonelyplanet.com/destinations. The reason for choosing the dataset is to scrutinize the presentation of semantic relation extraction for more consideration. This dataset has
14. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
46
different customs of input and details. But we are choosing only the countries and cities related details and we processed those details only limit to other properties in the dataset. Thus the dataset is suitable for our proposed ARSRE technique.
5.2 Comparison
In our experiment, we compared the proposed ARSRE technique with state-of-art method like CIV Based Semantic Relation Extraction using Markov Logic Network (CIVSRE). The previous CIVSRE method contains the processes of first-order logic predicates and finding inference and learning weight based semantic relation extraction. Our proposed technique also does the same processes first-order logic predicates and finding inference and learning weight semantic extraction with some slight changes. The changes can be highlighted first in semantic relation extraction process to find the frequent items between the relationships we have to use Association Rule Mining. Second, to extensibility of existing lightweight ontologies to formal ontology. The input dataset can be loaded into the application. The dataset could be processed by the GATE tool. This GATE tool can take the data’s in corpus and processed by some processor of this tool and generate the corrected information of dataset. This input also enters into the progress for semantic relation extraction. The processed dataset will be used to construct the first-order logic predicates. For constructing the predicates, we have to use knowledge base (KB). This is mainly generated from the formulas of first order. These all are constructed with the help of type of symbols. Symbols consist of constants, variables, functions and predicates. The knowledge base formulas can be of two kinds of partitions. There are refutation and conjunctive normal form (CNF). A KB in clausal form is a conjunction of clauses, a clause being a disjunction of literals. Every KB in first-order logic can be converted to clausal form using a mechanical sequence of steps. Clausal form is used in resolution, a sound and refutation-complete inference procedure for first-order logic.
After performing this first-order logic we have to do some kind of building operations. First we build mln file, next build training data file and build the test files in the corpus. Here after do the process of identifying inference values and also weight learning. Our proposed technique can be producing the values of number of hidden predicates, number of formulas, evidence atoms and also optimal solution. Then next, process we were done in this extraction process is to find the frequent itemset relationship based upon the words. For that purpose we had to use association rule mining. According to that methodology every word in the dataset we have to know the relationship and also frequent items. Because frequent items are main thing in our proposed technique to find the relationship. Mining association rules basically consists of two portions first compute the frequent item sets respect to the minimum support, second one is generate rules from the frequent item sets according to the minimum confidence. As the number of frequent sets can be very large, closed and maximal item sets can be generated instead. Here, we use apriori algorithm to find the frequent item sets.
The final process of our proposed technique is to avoid non-useful words. Non-useful word means the word which is presented in the input file those are present meaningless. Those meaningless words are to be removed. For removal process we ensure the technology of conversion of lightweight ontology to formal ontology. To performing this step first we select all non useful words and avoid those words. Ontologies are fundamental to the success of the Semantic Web, as they enable software agents to exchange, share, and reuse. For the semantic web to function computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. For this reason, the majority of ontology learning systems out there that claim to learn ontologies are in fact, creating lightweight ontologies. At the moment, lightweight ontologies appear to be the most common type of ontologies in a variety of Semantic Web applications.
15. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
47
Technique
CIVSRE
ARSRE
Precision
119.85
233.33
Recall
32
44.142
F1-Measure
4,895
7,424
Table 1 Comparison of Both Techniques
We also need to avoid the redundant words in the semantic extraction. According to the comparison results we have to know the efficiency level of semantic relation extraction. The efficiency of proposed and previous methods are calculated from the above table.
5.3 Experimental Results
This experiment seeks to study and organize the concept of semantic relation extraction into the ontology learning. Figure 3 shows the Precision, Recall and F1-Measures for the proposed technique. The ARSRE technique was evaluated by comparing its output with a CIVSRE. For this purpose we use the data set of Lonely Planet corpus for performing the semantic extraction task. Here we evaluate the relations from the dataset of Lonely Planet corpus. This evaluated result is the semantic relation extraction using the ARSRE technique. This ARSRE technique is based on the Markov Logic Networks, which is performed by the Alchemy software packages, and also performed by association rule mining.
The ARSRE technique consists of the steps of first-order logic predicates and finding inference and learning weight based semantic relation extraction. Here the construction of predicates mainly focuses the Knowledge Base of the formulas. According to the knowledge base we have to construct the first-order logic predicates. After that we have to build the .mln file, build training file and also build the test file. Then next, we have to find the inference and weight learning of the contents, this is done with the help of Markov Logic Network. These are performed by the same method taken from the state-of-art method. Then the process include in our proposed technique is to avoid the non useful words. For that purpose we have to select the non-useful words and avoid the redundant words in the scenario and also to make the tree structure using thesaurus. These all processes are covered by the ARSRE techniques.
The experiment results can be compared with the technique of CIVSRE and ARSRE. The results can be varying with the parameter of accuracy based on relationship. Our process should produce more accuracy when compared to the preceding technique. The accuracy can be calculated with the help of the values of precision, recall and alsoF1 measure. Our proposed ARSRE technique can have the best precision, recall and also F1 measure. According to those values the accuracy of every technique shall be calculated.
16. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
48
Figure 3 Lonely Planet Corpus Relationship Results
Figure 4 Lonely planet corpus experimental results
6. CONCLUSION
This paper presented a framework of ARSRE for semantic relation extraction which applies Markov Logic Networks for predicting the predicates, first order logic formula and also inference and weight learning. While a matter between efficiency and accuracy we first proposed deterministic ARSRE technique by means of the first-order logic predicates and semantic relation extraction. To implementing this ARSRE technique we have to use the dataset of lonely planet corpus. The dataset having the details of tourism countries, cities and temples like that. According to the dataset we take the input of words and further processes are carried out by techniques. First process of our semantic relation extraction is prediction of first-order logic formula. This process
17. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
49
encourages the method of knowledge base. According to this knowledge base we have to construct the predicates. Because it might be used the knowledge base formula symbols. The symbols are carried out by many parts. There are constants, variables, functions and predicates. A term is any expression representing an object in the domain. It can be a constant, a variable, or a function applied to a tuple of terms. After that we have to build some kind of files such as .mln file, training file and also test files. Second process is find inference and weight learning we use MLN method to learning the weight of words. For that purpose we can use the simple weight learning method to produce the good results. The main progress include in that is to find the inference values. For finding the inference we could use alchemy process. It may be the software to produce the optimized values of every word in the corpus. Alchemy Packages also used for implement this identification process. Alchemy packages are used for make the perfect inference process. For implementing all this performance, we use the dataset of Lonely Planet. The final process of our ARSRE technique is the semantic relation extraction. In that state, need to do the efficient way of semantic relation extraction. For this reason we use Association Rule Mining for finding frequent items between the relationships of every input data. Performing this kind of process everyone use the Apriori algorithm and phrase based semantic relation identification processes. After performing that this proposed technique does the progress of avoidance of non- useful words. This could be performed with the help of ontology learning (OWL). Using this we can convert the extensibility of lightweight ontology into formal representation. An important benefit of a formal representation is the ability to specify axioms for reasoning in order to determine validity and to define constraints in ontologies. Moreover, a formal ontology is natural language independent or, in other words, does not contain lexical knowledge. Our ARSRE technique can do the process of association rule mining based semantic relation extraction. It could produce the efficient way of finding frequent item set between the relationship of every words.
7. FUTURE ENHANCEMENT
In our paper we present the process of semantic relations methodology named as ARSRE. This technique should provide the results of semantic relation efficiently. Association Rule Mining could be used to produce the relationships between the frequent items. Here, we could also did the process of selecting the non-useful words and avoid these words and make the tree structured results. This technique could be used to produce the better efficiency of semantic relations between concepts and methods. In future, to improve the ontology learning progress we have to move onto the process of axiom learning. Axiom learning will be based on rules come from the semantic relation. For axiom learning we use DRIC. The process of DRIC is to be discovery of inference rules from text. Using this process we have to make the tree structure of the resulted words. Next, we decide to do the process of ontology population. In ontology population mainly will be useful for knowledge acquisition. Knowledge Acquisition activities that transfer data or text into instance data. To open resultant instant data using Protégé tools. This ontology population is to analyze the population in semi automatically. These ideas are decided to implement in the future.
REFERENCES
[1] Hassan Khosravi,” Discriminative Strucure and parameter learning for Markov Logic Networks”, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
[2] Domingos and Richardson. 2007. Markov Logic: A Unifying Framework for Statistical Relational Learning. Introduction to Statistical Relational Learning (pp. 339 - 371). Cambridge, MA: MIT Press.
18. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
50
[3] M. Richardson, P. Domingos. Markov logic networks. Machine Learning, 2006, 62(1{2): 107 - 136.
[4] Dellschaft, K., Staab, S.: On how to perform a gold standard based evaluation of ontology learning. In: Proceedings of ISWC-2006 International Semantic Web Conference, 2006.
[5] Dr.V.Karthikeyani and K.Karthikeyan, “Migrate Web Documents into Web Data,” Electronics Computer Technology (ICECT) 3rd International Conference, 2011, Vol. 5, pp. 249 - 253.
[6] Athanasios Tegos, Vangelis Karkaletsis, and Alexandros Potamianos, “Learning of Semantic Relations between Ontology Concepts using Statistical Techniques”, proceeding at Technical University of Crete, july 27, 2012.
[7] Thomas Hofmann, “Probabilistic Latent Semantic Analysis,” In Proc. of Uncertainty in Artificial Intelligence, UAI’99, 1999, pp.289-296.
[8] Karthikeyan, K and Dr.V.Karthikeyani, “Understanding text using Anaphora Resolution”, Pattern Recognition, Informatics and Mobile Engineering (PRIME) 2013 International Conference, 2013, pp- 346 – 350.
[9] Wilson wong, wei liu and mohammed bennamoun, “Ontology Learning from Text: A Look Back and into the Future,” ACM Comput. Surv. 44, 4, August 2012, 36 pages.
[10] Paul Buitelaar, Philipp Cimiano and Bernardo Magnini, “Ontology Learning from Text: An Overview,” in Applications and Evaluation, 2005, pp. 3--12.
[11] Lucas Drumond and Rosario Girardi, “A Survey of Ontology Learning Procedures,” WONTO, volume 427 of CEUR Workshop Proceedings, CEUR-WS.org, 2008.
[12] Lucas Drumond and Rosario Girardi, “An Experiment Using Markov Logic Networks to Extract Ontology Concepts from Text,” in ACM Special Interest Group on Applied Computing, 2010, pp. 1354-1358.
[13] Stanley Kok, Parag Singla, Matthew Richardson and Pedro Domingos, “The Alchemy System for Statistical Relational AI: User Manual”, proceeding at University of Washington, Department of Computer Science and Engineering, Aug 3, 2007.
[14] Zhen LI, Janyi GUO, Zhengtao, Yantuan XIAN, Xin YAN and Sichao WEI, “The CIV Semantic Relations Extraction based on Markov Logic Networks”, Kunming University of Science and Technology, proceedings of the Journal of Computational Information Systems 2013.
[15] A.Matthew Richardson and Pedro Domingos, “Markov Logic Networks,” 2006, vol. 6, pp. 1–44. [16] Fei Wu, Daniel S. Weld, “Automatically Refining the Wikipedia Infobox Ontology”, 17th international conference on World Wide Web, pp- 635-644, 2008.
[17] Quang-Thang DINH, Christel Vrain and Matthieu Exbrayat, ”Generative Structure Learning for Markov Logic Network Based on Graph of Predicates”, IJCAI'11 Proceedings of the Twenty- Second international joint conference on Artificial Intelligence – Vol. 2, pp. 1249-1254.
[18] Xiangfeng Luo, Kai Yan and Xue Chen, “Automatic Discovery of Semantic Relations Based on Association Rule” journal of software, vol. 3, no. 8, november 2008.
[19] W. Kang, Z. F. Sui. Synchronization Extraction of Ontology Concept Instances and the Attributes Based onThe Weak Guidance Web. Journal of Chinese Information, 2010. 6: 54 - 59.
[20] Kaustubh Beedkar, Luciano Del Corro, Rainer Gemulla, “Fully Parallel Inference in Markov Logic Networks”, proceeding at Max-Planck-Institut für Informatik, vol.2, 2011, pp- 373-384 .
[21] Hilda Koopman, Dominique Sportiche and Edward Stabler, “An Introduction to Syntactic Analysis and Theory”, 2013.
[22] Sandeepkumar Satpal, Sahely Bhadra, S Sundararajan, Rajeev Rastogi and Prithviraj Sen, “Web Information Extraction Using Markov Logic Networks”, 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp- 1406-1414. [23] Alexander Maedche and Steffen Staab, “Ontology Learning for the Semantic Web”, Intelligent Systems, vol.16, pp- 72-79, 2011.
[24] M. Richardson, P. Domingos. Markov logic networks. Machine Learning, 2006, 62(1{2): 107 - 136.
[25] Hilda Koopman, Dominique Sportiche and Edward Stabler, “An Introduction to Syntactic Analysis and Theory”, Proceedings of the 14th conference on Computational linguistics, vol. 2, Jan. 1992, pp. 539-545.
19. International Journal of Web & Semantic Technology (IJWesT) Vol.5, No.4, October 2014
51
[26] Hoifung Poon Pedro Domingos, “Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1,pp. 458-463.
[27] K.Karthikeyan and Dr.V.Karthikeyani, “PROCEOL: Probabilistic Relational of Concept Extraction in Ontology Learning”, Internation Review on Computers and Software, Vol.9, No.4, 2014.
[28] Hamish Chunningham, Yorick Wilks and Robert J.Gaizauskas, “A General Architecture for Text Engineering”, proceeding at an institute of language, speech and hearing, 2012.
[29] M. Poesio, A. Almuhareb, Identifying Concept Attributes Using a Classifier, Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition, Ann Arbor, 2005: 8 - 27.
[30] J. Y. Guo, Z. Li, Z. T. Yu, etc. Extraction of Ontology Concept Instance, Attribute, Attribute Values and Their Relationship [J]. Journal of Nanjing University (Natural Sciences), 2012(48): 1 - 7.
Authors
KARTHIKEYAN K was born in Tamil Nadu, India in 13th February 1975. He is working as Assistant Professor in Department of Information Technology at NPR Arts & Science College, Natham, Tamil Nadu, India. He was received M.Sc degree in Information Technology from Bharathidasan University, Tamil Nadu, India in 2002, M.Phil degree from Madurai Kamraj University, Tamil Nadu, India in 2006. He has published 2 National and 2 International Conferences Papers and One International Journals and presented papers in National and International Conferences. He has 10 years teaching experience. His areas of Interests are Data Mining, Artificial Intelligence, Natural Language Processing, Data and Knowledge Engineering.
Dr. KARTHIKEYANI V was born in Tamil Nadu, India in 11th June 1972. She is working as Assistant Professor in Department of Computer Science at Govt. Arts College, Rasipuram, Tamilnadu, India. She was awarded Doctoral degree from Periyar Universtiy, Salem, Tamilnadu, India. She has published 15 National and International Journals and presented several papers in International and National Conferences. She has 17 years of teaching experience. Her areas of interests are Image Processing, Computer Graphics, Multimedia, Data Mining and Web Mining. She is a life member in Computer Society of India (CSI), ISTE (Indian Society for Technical Education), ACM-CSTA (Computer Science Teacher Association) and various other International Computer Societies and organization for knowledge exchange and enhancement.