Pub Date : 2010-03-01DOI: 10.4018/978-1-61520-859-3.CH002
W. Jaziri, F. Gargouri
{"title":"Ontology Theory, Management and Design","authors":"W. Jaziri, F. Gargouri","doi":"10.4018/978-1-61520-859-3.CH002","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH002","url":null,"abstract":"","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131160865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH012
Antonio Jimeno-Yepes, Rafael Berlanga Llavori, D. Rebholz-Schuhmann
Ontologies represent domain knowledge that improves user interaction and interoperability between applications. In addition, ontologies deliver precious input to text mining techniques in the biomedical domain, which might improve the performance in different text mining tasks. This chapter will explore on the mutual benefits for ontologies and text mining techniques. Ontology development is a time consuming task. Most efforts are spent in the acquisition of terms that represent concepts in real life. This process can use the existing scientific literature and the World Wide Web. The identification of concept labels, i.e. terms, from these sources using text mining solutions improves ontology development since the literature resources make reference to existing terms and concepts. Furthermore, automatic text processing techniques profit from ontological resources in different tasks, for example in the disambiguation of terms and the enrichment of terminological resources for the text mining solution. One of the most important text mining tasks that exploits ontological resources consists of the mapping of concepts to terms in textual sources (e.g. named entity recognition, semantic indexing) and the expansion of queries in information retrieval. DOI: 10.4018/978-1-61520-859-3.ch012
{"title":"Applications of Ontologies and Text Mining in the Biomedical Domain","authors":"Antonio Jimeno-Yepes, Rafael Berlanga Llavori, D. Rebholz-Schuhmann","doi":"10.4018/978-1-61520-859-3.CH012","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH012","url":null,"abstract":"Ontologies represent domain knowledge that improves user interaction and interoperability between applications. In addition, ontologies deliver precious input to text mining techniques in the biomedical domain, which might improve the performance in different text mining tasks. This chapter will explore on the mutual benefits for ontologies and text mining techniques. Ontology development is a time consuming task. Most efforts are spent in the acquisition of terms that represent concepts in real life. This process can use the existing scientific literature and the World Wide Web. The identification of concept labels, i.e. terms, from these sources using text mining solutions improves ontology development since the literature resources make reference to existing terms and concepts. Furthermore, automatic text processing techniques profit from ontological resources in different tasks, for example in the disambiguation of terms and the enrichment of terminological resources for the text mining solution. One of the most important text mining tasks that exploits ontological resources consists of the mapping of concepts to terms in textual sources (e.g. named entity recognition, semantic indexing) and the expansion of queries in information retrieval. DOI: 10.4018/978-1-61520-859-3.ch012","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115107877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH011
J. Nogueras-Iso, J. Lacasta, J. Teller, G. Falquet, J. Guyot
Ontology learning is the term used to encompass methods and tech- niques employed for the (semi-)automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology construction. This chapter focuses on ontology learning techniques using thesauri as input sources. Thesauri are one of the most promising sources for the creation of domain ontologies thanks to the richness of term definitions, the existence of a priori relationships be- tween terms, and the consensus provided by their extensive use in the library con- text. Apart from reviewing the state of the art, this chapter shows how ontology learning techniques can be applied in the urban domain for the development of domain ontologies.
{"title":"Ontology Learning from Thesauri","authors":"J. Nogueras-Iso, J. Lacasta, J. Teller, G. Falquet, J. Guyot","doi":"10.4018/978-1-61520-859-3.CH011","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH011","url":null,"abstract":"Ontology learning is the term used to encompass methods and tech- niques employed for the (semi-)automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology construction. This chapter focuses on ontology learning techniques using thesauri as input sources. Thesauri are one of the most promising sources for the creation of domain ontologies thanks to the richness of term definitions, the existence of a priori relationships be- tween terms, and the consensus provided by their extensive use in the library con- text. Apart from reviewing the state of the art, this chapter shows how ontology learning techniques can be applied in the urban domain for the development of domain ontologies.","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133803741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH010
Najla Sassi, Zouhaier Brahmia, W. Jaziri, R. Bouaziz
In computer science, several application areas are especially concerned with versioning such as software development, CAD/CAM applications, temporal databases and ontologies. Temporal databases support time-varying information and maintain the history of the modelled data (Jensen et al., 1998) (Özsoyoğlu et al., 1995) (Tansel et al., 1993). Versions of temporal data are kept along one or two time dimensions: valid time and transaction time (Jensen et al., 1998). The valid time concerns the time of the modelled real world and denotes the time a fact was, or will be true, whereas the transaction time is the one of the system and concerns the one during which the fact was or is current in the database as a stored data. AbsTRACT
在计算机科学中，有几个应用领域特别关注版本控制，如软件开发、CAD/CAM应用、时态数据库和本体。时态数据库支持时变信息并维护建模数据的历史(Jensen et al.， 1998) (Özsoyoğlu et al.， 1995) (Tansel et al.， 1993)。时间数据的版本沿着一个或两个时间维度保存:有效时间和事务时间(Jensen et al.， 1998)。有效时间与建模的真实世界的时间有关，表示事实过去为真或将为真的时间，而事务时间是系统的时间，与事实作为存储数据在数据库中是或当前的时间有关。摘要
{"title":"From Temporal Databases to Ontology Versioning","authors":"Najla Sassi, Zouhaier Brahmia, W. Jaziri, R. Bouaziz","doi":"10.4018/978-1-61520-859-3.CH010","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH010","url":null,"abstract":"In computer science, several application areas are especially concerned with versioning such as software development, CAD/CAM applications, temporal databases and ontologies. Temporal databases support time-varying information and maintain the history of the modelled data (Jensen et al., 1998) (Özsoyoğlu et al., 1995) (Tansel et al., 1993). Versions of temporal data are kept along one or two time dimensions: valid time and transaction time (Jensen et al., 1998). The valid time concerns the time of the modelled real world and denotes the time a fact was, or will be true, whereas the transaction time is the one of the system and concerns the one during which the fact was or is current in the database as a stored data. AbsTRACT","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126699572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH001
Fabien L. Gandon
{"title":"Ontologies in Computer Science","authors":"Fabien L. Gandon","doi":"10.4018/978-1-61520-859-3.CH001","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH001","url":null,"abstract":"","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123687517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH003
C. Jouis, Julien Bourdaillet, Bassel Habib, J. Ganascia
This chapter is a contribution to the study of formal ontologies. It addresses the problem of atypical entities in ontologies. The authors propose a new model of knowledge representation by combining ontologies and topology. In order to represent atypical entities in ontologies, the four topological operators of interior, exterior, border and closure are introduced. These operators allow to specify whether an entity, belonging to a class, is typical or not. The authors define a system of topological inclusion and membership relations into the ontology formalism, by adapting the four topological operators with the help of their mathematical properties. These properties are used as a set of axioms which allows to define the topological inclusion and membership relations. Further, the authors define combinations of the operators of interior, exterior, border and closure that allow the construction of an algebra. They model is implemented in AnsProlog, a recent logic programming language that allows negative predicates in inference rules.
{"title":"Exceptions in Ontologies","authors":"C. Jouis, Julien Bourdaillet, Bassel Habib, J. Ganascia","doi":"10.4018/978-1-61520-859-3.CH003","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH003","url":null,"abstract":"This chapter is a contribution to the study of formal ontologies. It addresses the problem of atypical entities in ontologies. The authors propose a new model of knowledge representation by combining ontologies and topology. In order to represent atypical entities in ontologies, the four topological operators of interior, exterior, border and closure are introduced. These operators allow to specify whether an entity, belonging to a class, is typical or not. The authors define a system of topological inclusion and membership relations into the ontology formalism, by adapting the four topological operators with the help of their mathematical properties. These properties are used as a set of axioms which allows to define the topological inclusion and membership relations. Further, the authors define combinations of the operators of interior, exterior, border and closure that allow the construction of an algebra. They model is implemented in AnsProlog, a recent logic programming language that allows negative predicates in inference rules.","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129740548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH004
Y. Pollet
We address in this chapter the problem of the autom ated discovery and composition of Web Services. Now, Service-oriented computing is emergi ng as a new and promising paradigm. However, selection and composition of Services to a chieve an expected goal remain purely manual and time consuming tasks. Basing our approac h n domain concept definitions thanks to an Ontology, we develop here an algebraic approach that enables to express formal definitions of Web Service semantics as well as user information n eeds. Both are captured by the means of algebraic expressions of ontology properties. We pr esent an algorithm that generates efficient orchestration plans, with characteristics of optima l ty regarding Quality of Service. The approach has been validated by a prototype and an evaluation in the case of an Health Information System. INTRODUCTION The number of available Web data sources and servic es has exploded during the last years. This enables users to access rich information in many do mains such as health, life sciences, law, geography, and many other domain of interest. Thank s to this wealth, users rely more on various digital tasks such as data retrieval from both publ ic and corporate data sources and data analysis with Web tools or services organized in complex wor kfl ws [Gao, 2005, Kinsi,2007]. However, human users have to spend uncountable hours to expl r and discover Web resources that meet their requirements. In addition, in many cases, use rs n ed to compose a specific set of Web resources in order to fulfill a complex question. T his situation is mainly due to the inability of present standards in capturing Web Service semantic s, i.e. the precise meaning of what a given Web Service exactly delivers regarding a specific u ser context. Meanwhile, Service-oriented computing (SoC) is emer ging as a new and promising computing paradigm that centers on the notion of service as t he fundamental element for accessing heterogeneous, rich and distributed resources in an teroperable way [Roman, 2005]. Web services are self-describing components that support a rapid and significant reuse of distributed applications. They are offered by service providers , which procure service implementation and maintenance, and supply service descriptions. Servi ce descriptions are used to advertise service capabilities, behavior, Quality of Service, etc. (U DDI, WSDL, OWL-S). Service descriptions are meant to be used by other applications (and possibl y other services), and not only by humans. WSDL and UDDI are the basic standards used for Web Service capabilities descriptions and advertising. However, they focus on the description of interfaces and syntactic considerations. So, at present, the development of powerful applica tions on the Web is still facing two major problems. The first one is related to the increasin g difficulties of identifying services that
{"title":"An Algebra of Ontology Properties for Service Discovery and Composition in Semantic Web","authors":"Y. Pollet","doi":"10.4018/978-1-61520-859-3.CH004","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH004","url":null,"abstract":"We address in this chapter the problem of the autom ated discovery and composition of Web Services. Now, Service-oriented computing is emergi ng as a new and promising paradigm. However, selection and composition of Services to a chieve an expected goal remain purely manual and time consuming tasks. Basing our approac h n domain concept definitions thanks to an Ontology, we develop here an algebraic approach that enables to express formal definitions of Web Service semantics as well as user information n eeds. Both are captured by the means of algebraic expressions of ontology properties. We pr esent an algorithm that generates efficient orchestration plans, with characteristics of optima l ty regarding Quality of Service. The approach has been validated by a prototype and an evaluation in the case of an Health Information System. INTRODUCTION The number of available Web data sources and servic es has exploded during the last years. This enables users to access rich information in many do mains such as health, life sciences, law, geography, and many other domain of interest. Thank s to this wealth, users rely more on various digital tasks such as data retrieval from both publ ic and corporate data sources and data analysis with Web tools or services organized in complex wor kfl ws [Gao, 2005, Kinsi,2007]. However, human users have to spend uncountable hours to expl r and discover Web resources that meet their requirements. In addition, in many cases, use rs n ed to compose a specific set of Web resources in order to fulfill a complex question. T his situation is mainly due to the inability of present standards in capturing Web Service semantic s, i.e. the precise meaning of what a given Web Service exactly delivers regarding a specific u ser context. Meanwhile, Service-oriented computing (SoC) is emer ging as a new and promising computing paradigm that centers on the notion of service as t he fundamental element for accessing heterogeneous, rich and distributed resources in an teroperable way [Roman, 2005]. Web services are self-describing components that support a rapid and significant reuse of distributed applications. They are offered by service providers , which procure service implementation and maintenance, and supply service descriptions. Servi ce descriptions are used to advertise service capabilities, behavior, Quality of Service, etc. (U DDI, WSDL, OWL-S). Service descriptions are meant to be used by other applications (and possibl y other services), and not only by humans. WSDL and UDDI are the basic standards used for Web Service capabilities descriptions and advertising. However, they focus on the description of interfaces and syntactic considerations. So, at present, the development of powerful applica tions on the Web is still facing two major problems. The first one is related to the increasin g difficulties of identifying services that","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH013
Mihaela Brut, F. Sèdes
The multimedia indexing and management is a very important issue in the actual context where various domains such as news gathering, TV, banks of resources for commercial or consumer applications, collaborative work, video surveillance are flooded by a huge amount of multimedia sources. The traditional multimedia indexation techniques are focused on the effective multimedia content processing, being mainly in charge with low-level multimedia features analysis. They could capture some information about the content description (such as shapes or faces recognition), but not in terms of high-level concepts (such as ontology or vocabulary concepts). The chapter is focused on possible solutions for the problem of bridging the “semantic gap” between low-level multimedia AbsTRACT
{"title":"Ontology Based Multimedia Indexing","authors":"Mihaela Brut, F. Sèdes","doi":"10.4018/978-1-61520-859-3.CH013","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH013","url":null,"abstract":"The multimedia indexing and management is a very important issue in the actual context where various domains such as news gathering, TV, banks of resources for commercial or consumer applications, collaborative work, video surveillance are flooded by a huge amount of multimedia sources. The traditional multimedia indexation techniques are focused on the effective multimedia content processing, being mainly in charge with low-level multimedia features analysis. They could capture some information about the content description (such as shapes or faces recognition), but not in terms of high-level concepts (such as ontology or vocabulary concepts). The chapter is focused on possible solutions for the problem of bridging the “semantic gap” between low-level multimedia AbsTRACT","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123651354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH005
Thabet Slimani, B. B. Yaghlane, K. Mellouli
Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness. DOI: 10.4018/978-1-61520-859-3.ch005
{"title":"Approaches for Semantic Association Mining and Hidden Entities Extraction in Knowledge Base","authors":"Thabet Slimani, B. B. Yaghlane, K. Mellouli","doi":"10.4018/978-1-61520-859-3.CH005","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH005","url":null,"abstract":"Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness. DOI: 10.4018/978-1-61520-859-3.ch005","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133150038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-61520-859-3.CH009
S. Sellami, A. Benharkat, Y. Amghar
Nowadays, the Information technology domains (semantic web, E-business, digital libraries, life science, etc) abound with a large variety of data (e.g. DB schemas, XML schemas, ontologies) and bring up a hard problem: the semantic heterogeneity. Matching techniques are called to overcome this challenge and attempts to align these data. In this chapter, the authors are interested in studying large scale matching approaches. They survey the techniques of large scale matching, when a large number of schemas/ontologies and attributes are involved. They attempt to cover a variety of techniques for schema matching called Pair-wise and Holistic, as well as a set of useful optimization techniques. They compare the different existing schema/ontology matching tools. One can acknowledge that this domain is on top of effervescence and large scale matching needs many more advances. Then the authors provide conclusions concerning important open issues and potential synergies of the technologies presented.
{"title":"Large Scale Matching Issues and Advances","authors":"S. Sellami, A. Benharkat, Y. Amghar","doi":"10.4018/978-1-61520-859-3.CH009","DOIUrl":"https://doi.org/10.4018/978-1-61520-859-3.CH009","url":null,"abstract":"Nowadays, the Information technology domains (semantic web, E-business, digital libraries, life science, etc) abound with a large variety of data (e.g. DB schemas, XML schemas, ontologies) and bring up a hard problem: the semantic heterogeneity. Matching techniques are called to overcome this challenge and attempts to align these data. In this chapter, the authors are interested in studying large scale matching approaches. They survey the techniques of large scale matching, when a large number of schemas/ontologies and attributes are involved. They attempt to cover a variety of techniques for schema matching called Pair-wise and Holistic, as well as a set of useful optimization techniques. They compare the different existing schema/ontology matching tools. One can acknowledge that this domain is on top of effervescence and large scale matching needs many more advances. Then the authors provide conclusions concerning important open issues and potential synergies of the technologies presented.","PeriodicalId":169003,"journal":{"name":"Ontology Theory, Management and Design","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133431907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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