The document discusses the TaToo semantic framework, which aims to improve discovery of environmental resources by semantically annotating them. It does this using a hybrid ontology approach, with a shared "bridge" ontology and linked domain-specific ontologies. This allows formal annotations that machines can understand, while integrating different sub-domains. The current framework links geospatial and temporal ontologies through the bridge ontology to enable cross-domain search based on location and time.
This document discusses an integrated approach to ontology development methodology and provides a case study using a shopping mall domain. It begins by reviewing existing ontology development methodologies and identifying their pitfalls. An integrated methodology is then proposed which aims to reduce these pitfalls. The key steps in the proposed methodology are: 1) capturing motivating user scenarios or keywords, 2) generating formal/informal questions and answers from the scenarios, 3) extracting terms and constraints, and 4) building the ontology using a top-down approach. The methodology is applied to developing an ontology for a shopping mall domain to provide multilingual information to visitors.
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.
Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...IOSR Journals
Text2Onto is a tool that learns ontologies from textual data by extracting ontology components like concepts, relations, instances, and hierarchies. It analyzes texts through linguistic preprocessing using Gate to tokenize, tag parts of speech, and identify noun and verb phrases. Algorithms then extract ontology components and store them probabilistically in a Preliminary Ontology Model independent of any representation language. The study aimed to understand Text2Onto's architecture, analyze errors in its extractions, and attempt improvements by using a meta-model of the text to better classify concepts under core concepts.
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.
This document provides a review of existing approaches to ontology evolution and versioning. It begins by defining ontologies and discussing why evolution and versioning are needed as ontologies are used in information systems. It then outlines some existing solutions for ontology evolution and version management, noting different languages used to conceptualize ontologies. Challenges of ontology versioning and evolution are discussed. Increased usage of ontologies in different domains is reviewed. Finally, some available tools for ontology change management are mentioned.
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.
SWSN UNIT-3.pptx we can information about swsn professionalgowthamnaidu0986
Ontology engineering involves constructing ontologies through various methods. It begins with defining the scope and evaluating existing ontologies for reuse. Terms are enumerated and organized in a taxonomy with defined properties, facets, and instances. The ontology is checked for anomalies and refined iteratively. Popular tools for ontology development include Protege and WebOnto. Methods like Meth ontology and On-To-Knowledge methodology provide processes for building ontologies from scratch or reusing existing ones. Ontology sharing requires mapping between ontologies to allow interoperability, and libraries exist for storing and accessing ontologies.
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
This document discusses an integrated approach to ontology development methodology and provides a case study using a shopping mall domain. It begins by reviewing existing ontology development methodologies and identifying their pitfalls. An integrated methodology is then proposed which aims to reduce these pitfalls. The key steps in the proposed methodology are: 1) capturing motivating user scenarios or keywords, 2) generating formal/informal questions and answers from the scenarios, 3) extracting terms and constraints, and 4) building the ontology using a top-down approach. The methodology is applied to developing an ontology for a shopping mall domain to provide multilingual information to visitors.
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.
Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...IOSR Journals
Text2Onto is a tool that learns ontologies from textual data by extracting ontology components like concepts, relations, instances, and hierarchies. It analyzes texts through linguistic preprocessing using Gate to tokenize, tag parts of speech, and identify noun and verb phrases. Algorithms then extract ontology components and store them probabilistically in a Preliminary Ontology Model independent of any representation language. The study aimed to understand Text2Onto's architecture, analyze errors in its extractions, and attempt improvements by using a meta-model of the text to better classify concepts under core concepts.
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.
This document provides a review of existing approaches to ontology evolution and versioning. It begins by defining ontologies and discussing why evolution and versioning are needed as ontologies are used in information systems. It then outlines some existing solutions for ontology evolution and version management, noting different languages used to conceptualize ontologies. Challenges of ontology versioning and evolution are discussed. Increased usage of ontologies in different domains is reviewed. Finally, some available tools for ontology change management are mentioned.
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.
SWSN UNIT-3.pptx we can information about swsn professionalgowthamnaidu0986
Ontology engineering involves constructing ontologies through various methods. It begins with defining the scope and evaluating existing ontologies for reuse. Terms are enumerated and organized in a taxonomy with defined properties, facets, and instances. The ontology is checked for anomalies and refined iteratively. Popular tools for ontology development include Protege and WebOnto. Methods like Meth ontology and On-To-Knowledge methodology provide processes for building ontologies from scratch or reusing existing ones. Ontology sharing requires mapping between ontologies to allow interoperability, and libraries exist for storing and accessing ontologies.
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
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
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.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
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.
Recruitment Based On Ontology with Enhanced Security Featurestheijes
This document describes a recruitment system based on ontology with enhanced security features. The system allows human resource personnel to search for and select candidates based on criteria like area of interest and academic performance. HR users must first complete a registration process that generates a random security code sent to their email. They can then log in to search candidate profiles and select individuals of interest. Selected candidates' details are emailed to HR for future reference. The system also periodically refreshes candidate data to improve memory management and logs all user activity for security. The proposed system aims to facilitate secure, efficient recruitment while maintaining data integrity through its design.
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
This document provides a comparative study of four popular ontology building tools: Protégé 3.4, IsaViz, Apollo, and SWOOP. It discusses the features and functionalities of each tool, including their capabilities for ontology editing, browsing, documentation, import/export of formats, and visualization. The document aims to identify existing ontology tools that are freely available and can be used to develop ontologies for various application domains such as transport, tourism, health, and natural language. It evaluates the tools based on criteria like interoperability, openness, ease of updating/maintaining ontologies, and market penetration.
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.
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.
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...ijcsit
The process of building ontology is a very
complex and time
-
consuming process
especially when dealing
with huge amount of data. Unfortunately current
marketed
tools are very limited and don’t meet
all
user
needs.
Indeed, t
hese software build the core of the ontology from initial data that generates
a
big number of
information.
In this paper, we
aim to resolve these problems
by adding an extension to the well known
ontology editor Protégé in order to work towards a complete
FCA
-
based framework
which resolves the
limitation of other tools in
building fuzzy
-
ontology
.
W
e will give
, in this paper
, some
details on
our
sem
i
-
automat
ic collaborative tool
called FOD Tab Plug
-
in
which
takes into consideration another degree of
granularity in the process of generation
.
In fact, i
t follows a bottom
-
up strategy based on conceptual
clustering, fuzzy logic and Formal Concept Analysis (FCA) a
nd it defines ontology between classes
resulting from a preliminary classification of data and not from the initial large amount of data
.
The document discusses a novel domain ontology discovery method that exploits contextual information from knowledge sources to construct domain ontologies. It involves parsing text, identifying lexical patterns, extracting linguistic patterns, performing statistical token analysis using mutual information, and developing a taxonomy of domain concepts. The proposed method aims to assist in building domain ontologies more quickly and accurately compared to existing methods.
The document discusses the need for a human-centered methodology called HCOME for developing and managing ontologies. HCOME aims to actively involve knowledge workers in all stages of ontology development and evolution to better support their work. It emphasizes empowering knowledge workers to shape their shared information spaces by managing formal conceptualizations as part of their daily activities. The paper presents scenarios illustrating the need for knowledge workers to update ontologies and capture new knowledge. It then introduces the HCOME methodology and HCONE environment for implementing HCOME to seamlessly integrate ontology engineering into knowledge workers' workflows.
An adaptation of Text2Onto for supporting the French language IJECEIAES
The ontologies are progressively imposing themselves in the field of knowledge management. While the manual construction of an ontology is by far the most reliable, this task has proved to be too tedious and expensive. To assist humans in the process of building an ontology, several tools have emerged proposing the automatic or semi-automatic construction of ontologies. In this context, Text2Onto has become one of the most recognized ontology learning tools. The performance of this tool is confirmed by several research works. However, the development of this tool is based on Princeton WordNet (PWN) for English. As a result, it is limited to the processing of textual resources written in English. In this paper, we present our approach based on JWOLF, a Java API to access the free WordNet for French that we have developed to adapt this tool for the construction of ontologies from corpus in French. To evaluate the usefulness of our approach, we assessed the performance of the improved version of Text2Onto on a simplistic corpus of French language documents. The results of this experiment have shown that the improved version of Text2Onto according to our approach is effective for the construction of an ontology from textual documents in the French language.
Ontology Construction from Text: Challenges and TrendsCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. In light of the importance of ontology, different methodologies for building ontologies have been proposed. Ontology construction is a difficult and time-consuming process. Many efforts have been made to help ontology engineers to construct ontologies and to overcome the bottleneck of knowledge acquisition. The aim of this paper is to give a brief overview of ontology learning approaches and to review some of ontology extraction systems and tools followed by a summarizing comparison of them. Also some of the current issues and main trends of ontology construction from texts will be discussed.
An Incremental Method For Meaning Elicitation Of A Domain OntologyAudrey Britton
This document describes MELIS (Meaning Elicitation and Lexical Integration System), a tool developed to support the annotation of data sources with conceptual and lexical information during the ontology generation process in MOMIS (Mediator envirOnment for Multiple Information Systems). MELIS improves upon MOMIS by enabling more automated annotation of data sources and by extracting relationships between lexical elements using lexical and domain knowledge to provide a richer semantics. The document outlines the MOMIS architecture integrated with MELIS and explains how MELIS supports key steps in the ontology creation process, including source annotation, common thesaurus generation, and relationship extraction.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
Ontology learning techniques and applications computer science thesis writing...Tutors India
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The document summarizes a presentation on collaboration in organizations. It defines collaboration and discusses theories of collaboration. It also outlines different types of collaboration, principles of collaboration, tools that enable collaboration, and challenges in implementing collaboration within organizations. The document stresses that collaboration can help organizations be more agile, integrate information and processes more effectively, and optimize resource use.
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.
The document summarizes a seminar on ontology mapping presented by Samhati Soor. The seminar covered the need for ontology mapping due to the proliferation of ontologies, and the purpose of mapping ontologies to achieve interoperability and sharing knowledge. It defined ontologies and ontology mapping and discussed categories of mapping including between global and local ontologies, between local ontologies, and for merging ontologies. Tools for ontology mapping discussed included GLUE and SAM. Evaluation criteria and challenges of ontology mapping were also summarized along with conclusions and references.
The document proposes creating an Agricultural Ontology Service (AOS) to better organize information in the domain of food and agriculture. The AOS would provide a centralized access point and knowledge organization framework to improve information retrieval, make relevant information sources more accessible, and allow for interoperability across domains. It discusses using ontologies and semantic web technologies to formalize relationships between concepts to help machines better understand information.
Evaluating Scientific Domain Ontologies for the Electromagnetic Knowledge Dom...dannyijwest
The adoption of ontologies as a formalized approach to information codification is a constant-growing
phenomenon in scientific research. Moreover, knowledge sharing and reuse can be improved by adopting
hierarchical and modular frameworks and therein embedding available ontologies. Unfortunately, merging
procedures may bring about severe, time-consuming problems if a careful selection process is not carried
out. Based on these considerations, we propose a methodology for evaluating and selecting higher-level
ontologies, given the lower-level ones.
50 Self Evaluation Examples, Forms Questions TemplatAndrew Molina
The family ecomap shows ED's nuclear family consisting of herself, her husband BD, and their two grown children who live independently. While ED has a strong relationship with her son who lives out of state, her relationship with her daughter who lives locally is tense due to issues with her daughter's new husband whom ED and her husband dislike. The ecomap identifies sources of support and stress for the family to help develop plans to improve relationships and reduce stresses.
The document provides a 5-step process for requesting writing assistance from HelpWriting.net. It explains how to 1) create an account, 2) complete an order form with instructions and deadlines, 3) review writer bids and qualifications and place a deposit, 4) ensure the paper meets expectations and authorize payment, and 5) request revisions to ensure satisfaction. It guarantees original, high-quality content or a full refund.
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Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
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.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
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.
Recruitment Based On Ontology with Enhanced Security Featurestheijes
This document describes a recruitment system based on ontology with enhanced security features. The system allows human resource personnel to search for and select candidates based on criteria like area of interest and academic performance. HR users must first complete a registration process that generates a random security code sent to their email. They can then log in to search candidate profiles and select individuals of interest. Selected candidates' details are emailed to HR for future reference. The system also periodically refreshes candidate data to improve memory management and logs all user activity for security. The proposed system aims to facilitate secure, efficient recruitment while maintaining data integrity through its design.
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
This document provides a comparative study of four popular ontology building tools: Protégé 3.4, IsaViz, Apollo, and SWOOP. It discusses the features and functionalities of each tool, including their capabilities for ontology editing, browsing, documentation, import/export of formats, and visualization. The document aims to identify existing ontology tools that are freely available and can be used to develop ontologies for various application domains such as transport, tourism, health, and natural language. It evaluates the tools based on criteria like interoperability, openness, ease of updating/maintaining ontologies, and market penetration.
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.
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.
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...ijcsit
The process of building ontology is a very
complex and time
-
consuming process
especially when dealing
with huge amount of data. Unfortunately current
marketed
tools are very limited and don’t meet
all
user
needs.
Indeed, t
hese software build the core of the ontology from initial data that generates
a
big number of
information.
In this paper, we
aim to resolve these problems
by adding an extension to the well known
ontology editor Protégé in order to work towards a complete
FCA
-
based framework
which resolves the
limitation of other tools in
building fuzzy
-
ontology
.
W
e will give
, in this paper
, some
details on
our
sem
i
-
automat
ic collaborative tool
called FOD Tab Plug
-
in
which
takes into consideration another degree of
granularity in the process of generation
.
In fact, i
t follows a bottom
-
up strategy based on conceptual
clustering, fuzzy logic and Formal Concept Analysis (FCA) a
nd it defines ontology between classes
resulting from a preliminary classification of data and not from the initial large amount of data
.
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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
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A Model For Semantic Annotation Of Environmental Resources The Tatoo Semantic Framework
1. A Model for Semantic Annotation of Environmental
Resources: The TaToo Semantic Framework
Tomás Pariente, José María Fuentes, María Angeles Sanguino, Sinan Yurtsever,
Giuseppe Avellino, Andrea E. Rizzoli, Saša Nešić
ATOS Origin, Madrid, Spain
{tomas.parientelobo, jose.fuentesl, maria.sanguino,
sinan.yurtsever}@atosresearch.eu
Telespazio, Rome, Italy
giuseppe.avellino@telespazio.com
IDSIA, Lugano, Switzerland
{andrea, sasa}@idsia.ch
Abstract. In the last years we have seen the release of huge amount of
resources in the environmental domain. The use of non-formal tags for
annotating resources allows simple categorization and search capabilities, but it
does not provide the means to create cross-domain annotation and discovery.
On the other hand, ontologies are a shared and formal conceptualization of a
given domain. The use of formal semantics allows taking advantage of the
reasoning and inference power of the ontologies to create richer resource
annotations enhancing the discovery process. In the environmental domain
there is a clear need of frameworks and tools allowing formal tagging and
discovery. In this paper we discuss about the definition of a Semantic
Framework helping on the tagging and discovery process of environmental
resources. Moreover, we also report on the definition of a model to describe
environmental resources allowing cross-domain annotation and search.
Keywords: environment, discovery, annotation, ontology, cross-domain, search,
tagging.
1 Introduction
Since its inception, the Web has brought a revolution on how to publish, transmit and
consume information. While at first the number of information publishers on the Web
was limited, now almost everyone publishes personal and / or professional
information on the Web. Much of this growth is due to the fact that the expertise
needed to publish information on the Web is quite small. Basically, publishers only
need to own information and organize it in a way that makes it possible to be accessed
by consumers. However, the ease of publication, which originally facilitated the
growth of the Web, is not necessarily matching the ease of discovery and access to the
data. In professional environments, where information needs are highly complex, it is
necessary that machines, and not just humans, understand the information published
2. 2 Tomás Pariente, José María Fuentes, María Angeles Sanguino, Sinan Yurtsever,
Giuseppe Avellino, Andrea E. Rizzoli, Saša Nešić
to improve its exploitation capabilities. To be able to allow for automated machine-
processing of information we need to introduce the concepts of meta-information and
semantics associated to it. And this is also the context for the TaToo project.
The TaToo project, which started in 2010, aims at improving the discovery and
exploitation capabilities of the published environmental information by providing a
way to semantically enrich environmental resources. TaToo is built on top of three
fundamental pillars:
A framework to create complex and formal annotations. Informal annotations are
relatively good for human interpretation, but fail to help machines to understand
the meaning and perform more advanced tasks based on those annotations.
Annotations should be then formal and shared in the domain.
Usage of ontologies to enhance discovery capabilities and to prevent
interoperability issues. TaToo provides an ontology framework that allows cross-
domain and cross-language tagging and search. Producing annotations is a time
consuming task. TaToo offers both the automatic harvesting of meta-information
from existing resources, and a manual interface for humans; both approaches are
based on the above mentioned ontology framework.
A community driven approach. TaToo pays special care in providing easy-to-use
and effective tagging interfaces to enable and facilitate the tagging process. TaToo
is strongly inspired by existing social bookmarking initiatives, such as reddit,
StumbleUpon, Digg, etc. In this sense, TaToo allows the user producing their own
annotations, and share them with the community, starting an information
enrichment cycle. On the other hand, TaToo differs from the aforementioned social
approaches as it is using formal semantic annotations.
This paper focuses on the TaToo ontology framework. The first objective of TaToo is
the establishment of an ontology framework that allows the production of formal
resource descriptions. Subsequently, the TaToo system is taking advantage of domain
ontologies and formal resource descriptions to improve the exploitation of
environmental resources by improving discovery processes, enhancing the resource
presentation and providing interoperability between different fields of the
environmental domain with the aim of allowing cross-domain search.
Much of the functionality provided by TaToo relies on ontologies. Ontology
development is a complex, time-consuming task involving different roles (ontology
developers and domain experts) and activities (requirements acquisition, design, etc.).
Therefore, from the ontological point of view, one of the biggest challenges of TaToo
is to use or produce a formal description of the environmental domain that is at the
same time good enough to cover the project objectives, and on the other hand simple
enough to avoid unnecessary complexity and gruesome maintenance. The
environmental domain is actually a very broad domain that includes several sub-
domains dealing with such diverse areas as climatology, soil composition, crop
management, etc.
Although there are ontologies dealing with environmental issues, presently there is
no encompassing ontology detailing such a broad domain that fits the needs of TaToo.
Therefore, a realistic objective of TaToo in this regard is to allow sub-domain experts
3. A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic
Framework 3
to model their own sub-domain and at the same time provide the maximum degree of
integration between such sub-domain models. Because of these reasons the ontology
framework should ensure semantic interoperability between different domain
ontologies while ensuring their modular nature.
2 Ontology Framework
There are several approaches to achieve semantic interoperability when dealing with
different ontologies. Watche [2] defined three ways to integrate ontologies by using
single ontology, multiple ontology or hybrid ontology approaches.
The single ontology approach uses one global ontology that provides a shared
vocabulary for the specification of the semantics. Thus, all information sources are
related to one ontology, which usually is a combination of several specialized
ontologies. This approach is suitable for solving integration problems where all
information sources to be integrated provide nearly the same view on a domain.
In the multiple ontology approach each information source is described by its own
ontology. The main advantage of this approach is that no common and minimal
ontology commitment about a global ontology is needed. To achieve a common
understanding of the information sources, defined ontologies must be related. To
relate several “source describing” ontologies, several inter-ontology mappings must
be introduced. In practice, these inter-ontology mappings are very difficult to define
because of the many semantic heterogeneity problems which may occur, and the
situation is aggravated when there are many domain ontologies to map.
The hybrid approach is based on using a common shared ontology and a set of
local or application ontologies that are mapped uniquely to the shared vocabulary. In
this case the local ontologies would extend the vocabulary to the needs of a given
domain and the interoperability is achieved based on the shared ontology. TaToo
follows the hybrid approach as shown in Fig. 1.
Fig. 1.TaToo high-level ontology framework
4. 4 Tomás Pariente, José María Fuentes, María Angeles Sanguino, Sinan Yurtsever,
Giuseppe Avellino, Andrea E. Rizzoli, Saša Nešić
In order to apply the hybrid approach, a bridge ontology that will become the upper
layer of the integrated ontology framework has to be developed. Our bridge ontology
includes concept and property definitions from widely adopted ontologies and
necessary concept definitions in order to ensure the mappings between domain
ontologies. Any domain ontology should be mapped properly to the bridge ontology
in order to be usable by the integrated ontology framework. So the system interacts
with the domain ontologies via the bridge ontology.
The mapping process is done manually by defining equivalency and subsumption
relationships between concepts, object properties, data properties and individuals of
bridge ontology and the domain ontology. In the end from the point of view of the
bridge ontology, the entire ontology framework is seen as one big knowledge base. In
Fig. 2, where the current version of the TaToo ontology framework is shown, some
example mappings between ontology elements from the bridge ontology (x-shaped
circles in green) and the domain ontologies (x-shaped circles is gray) are depicted to
represent the cross-domain alignment process.
Fig. 2. Current version of the TaToo ontology framework
Some of the selected ontologies included in the TaToo bridge ontology are
GeoNames, an ontology for representing geospatial expressions, and OWLTime, an
ontology for representing temporal expressions. There are already implemented
reasoners which especially support both ontologies for geospatial and temporal
reasoning. One of them is OWLIM, an OWL semantic repository, which supports
geospatial reasoning exclusively for the GeoNames ontology. As TaToo is expected
to offer users the possibility to create time and location based annotations and
temporal and spatial queries, these ontologies become an essential part of the bridge
ontology. In particular, OWLIM has been selected to be used as the semantic
repository and the availability geospatial reasoning has been one of the reasons for
this choice. OWLIM supports OWL2-RL, a web ontology language profile inspired
5. A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic
Framework 5
by rule based languages. OWL2-RL is much more efficient and scalable compared to
OWL2-DL but less expressive. This is the reason why OWL2-RL has been chosen.
Because of the constant incremental annotated data that will be manipulated, a need
for more efficient reasoning arose and OWL2-RL is aimed to cover these kinds of
needs.
In TaToo, there is also the need to uniformly describe environmental information
resources. We think of an environmental resource as a web resource (being a web
page, a document, a model, a service, etc.), which is identified by a URI. In this sense,
the Minimal Environmental Resource Model (MERM) is defined as a part of the
shared ontology that will contain the minimal set of cross-domain concepts and
properties. For future releases, the usage of an upper-level ontology to perform more
elaborate cross-domain mappings will be investigated. The Semantic Web for Earth
and Environmental Terminology (SWEET) [1], a widely-used set of ontologies
providing semantic descriptions of Earth system science, is a good candidate for the
TaToo ontology framework.
3 The Minimal Environmental Resource Model
A minimum resource model is defined as the largest common denominator between a
set of heterogeneous description formalisms related to a common resource. Minimum
resource models have been applied in other research areas with satisfactory results. In
[4] a description of an open system for web services publication and discovery based
on a description of a minimal service model resulting in an ontology for service
modelling called POSM is presented. Functionally, the minimum resource model
aims at a similar objective to the bridge ontology: providing a common framework
between different domain ontologies. However, conceptually, the minimum resource
model is an effort to identify a minimal model that, without limiting the expression of
specific domains, acts as a reference for past and future applications in a specific area.
This effort and referencing aim is what differentiates the bridge ontology of a
minimum resource model and is what makes it a valuable resource.
One of the most relevant parts of the TaToo bridge ontology is the Minimal
Environmental Resource Model (MERM). MERM describes the structure of
resources and annotations in TaToo, being a reference data model for both services
and user interfaces. As any other ontology developed in the scope of TaToo, MERM
has been developed following the NeOn methodology [3]. The NeOn methodology
encourages reusing as much as possible. To that extent, MERM contains, besides the
already mentioned POSM ontology, concepts from several other ontologies like
SIOC1
, FOAF2
, Dublin Core3
and O&M4
. Fig. 3 presents the main classes of MERM:
Resource, Annotation, and ResourceAccessInfo.
1
The SIOC initiative (Semantically-Interlinked Online Communities), http://www.sioc-
project.org/
2
The Friend of a Friend (FOAF) project, http://www.foaf-project.org/
3
Dublin Core metadata iniciative, http://dublincore.org/
6. 6 Tomás Pariente, José María Fuentes, María Angeles Sanguino, Sinan Yurtsever,
Giuseppe Avellino, Andrea E. Rizzoli, Saša Nešić
Fig. 3. MERM core classes and relations
The Resource class represents a resource in the TaToo system. It includes resource
management information as author, owner, provider, date of creation, etc. A Resource
usually has access information presented in the ResourceAccessInfo class. The
ResourceAccessInfo class contains all information needed to access a resource. This
information is very heterogeneous ranging from a simple URL to a complex WSDL
depending on the nature of the resource. Finally, a resource presents a set of
annotations, represented by the Annotation class. An annotation describes what a
resource is about. Except for some management information, the content of an
annotation depends on the type of the annotated resource. An annotation for a web
document would be different from an annotation for a web service, being the “some
resource is related to some domain concepts” the simplest kind of annotation.
Resources in TaToo are retrieved taking their annotations as basis. In the current
version, MERM distinguishes between three kinds of annotations:
TimeSeriesAnnotation that describes time series following the model proposed by
the Observations and Measurements Ontology.
WebServiceAnnotation that describes web services following the model proposed
by POSM.
4
Observations and Measurements ontology, http://seres.uni-
muenster.de/o&m/O&M_discussion_paper.pdf
7. A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic
Framework 7
WebAnnotation that describes web resources following the model proposed by
SIOC.
MERM is still work in progress so the addition of new models for several
resources is expected. Fig. 4 shows in details the model adopted for
TimeSeriesAnnotation.
Fig. 4.TimeSeriesAnnotation model
Finally, there is also a special kind of annotations, the evaluations, which are
annotations that relates to another annotation instead to a resource. Evaluations are
created by users in order to evaluate how accurate or useful a resource annotation is.
This information is used during the discovery process to improve discovery results
and accuracy.
4 Integrating Domains
The TaToo Ontology framework, as it was described in Section 2, comprises the
bridge ontology, a number of domain ontologies, and a number of alignment
ontologies (see Fig. 2). The framework is not limited by a number of different domain
ontologies, that is, by a number of different environmental sub-domains whose
resources are managed by the TaToo system. However, in order to be able to plug-in a
domain ontology in the TaToo ontology framework, the domain ontology needs to be
8. 8 Tomás Pariente, José María Fuentes, María Angeles Sanguino, Sinan Yurtsever,
Giuseppe Avellino, Andrea E. Rizzoli, Saša Nešić
accompanied by an appropriate mapping interface. This mapping interface is
identified in the framework as an alignment ontology. In this section we describe two
possible alignment/mapping strategies on the example of three domains ontologies,
specific to the validation scenarios of the TaToo project, namely the JRC, MU, and
AIT ontologies. The JRC ontology describes the agro-environmental domain, the MU
ontology describes the anthropogenic impacts of global climate change, and finally
the AIT ontology contains concepts related to the comparison of climatic conditions
in different regions. These three domain ontologies are considered to be an integral
part of the TaToo ontology framework thus justifying the proposed ontology
integration approach. Moreover, we also plan to use them in the evaluation of the
semantic tagging and discovery capabilities of the TaToo system.
The first mapping strategy, adopted for the JRC ontology, uses the rdfs:subClassOf
construct defined in the RDFS to map concepts of the domain ontology to the
concepts of the MERM and bridge ontology. For example, concepts describing some
domain specific environmental resources should be specified as sub-concepts of the
merm:Annotation concept or its sub-concepts (merm:TimeSeriesAnnotation,
merm:WebService-Annotation, and merm:WebAnnotation). Moreover, the concepts
introduced to conceptualize some domain specific features (topics), which are
supposed to be a part of the resources‟ annotations, should be defined as sub-concepts
of the bridge:Topic concept. To illustrate this mapping strategy on a practical
example, in Figure 5 we list a snippet of the JRC ontology mapping specifying a
resource annotation type that describes software development kits:
The second mapping strategy, used for the AIT and MU ontologies, assumes the
usage of the owl:equivalentClass and owl:sameAs constructs defined in OWL, plus
the skos:broadMatch and skos:narrowMatch constructs defined in the SKOS5
(Simple
Knowledge Organization System) vocabulary to map concepts and properties of a
domain ontology to the concepts and properties of the MERM and bridge ontology.
The difference between the OWL and the SKOS constructs is that the latter ones
provide a looser mapping. There is also a slight difference between the two OWL
constructs regarding the strength of the mapping they provide. The meaning of the
owl:equivalentClass is that the two concepts have the same set of individuals.
However, the owl:equivalentClass does not imply the concept equality. Concept
equality means that the concepts have the same intentional meaning. Real concept
equality can be expresses by the owl:sameAs construct. In Figure 6 we show a snippet
of the MU ontology mapping that specifies the mapping of the “Diagnose” concept
from the MU domain to the merm:Annotation concept of the MERM ontology.
5
http://www.w3.org/TR/skos-reference/
Fig. 5. A snippet of the JRC ontology mapping
9. A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic
Framework 9
The development of both bridge ontology (including MERM) and domain
ontologies (i.e., JRC, AIT, and MU) in the TaToo Ontology framework is an
evolutionary process. In case of domain ontologies we plan to extend them in future
by adding new domain specific concepts and relationships that are currently not
provided. In case of the bridge ontology we want to identify which of the concepts
from the domain ontologies can be generalized and potentially become members of
the MERM ontology.
5 Conclusions
In this paper we have presented the approach of the TaToo project to the
development of an ontology framework for the semantic annotation of environmental
resources. As the environmental domain is composed by a wide number of sub-
domains, we chose a hybrid approach to be able to integrate ontologies for the various
sub-domains. Such an approach required the development of a bridge ontology and a
series of alignment ontologies to map concepts from domain specific ontologies to the
bridge ontology. A minimum environmental resource model has also been developed
to be included in the bridge ontology in order to provide a minimal set of common
concept to be shared across the different domains and facilitate alignments.
The TaToo ontology framework is functional to the declared objectives of the
TaToo project that is to allow for the semantic annotation of environmental resources
in order to enhance and facilitate search and discovery.
Acknowledgements
The research leading to these results has received funding from the European
Community's Seventh Framework Programme (FP7/2007-2013) under Grant
Agreement Number 247893.
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Fig. 6. A snippet of the MU ontology mapping
10. 10 Tomás Pariente, José María Fuentes, María Angeles Sanguino, Sinan Yurtsever,
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