The Semantic Web: Research and Applications

Pérez, Asunción Gómez; Euzenat, Jérôme

doi:10.1007/b136731

Cited by 14 publications

(12 citation statements)

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“…In the literature, maintaining consistency is based on the principles of resolution presented in (Haase and Stojanovic, 2005). When the axioms of change applied lead to an inconsistent ontology, the inconsistency is localized and the axiom having the lowest degree of confidence is identified and deleted.…”

Section: Related Workmentioning

confidence: 99%

Inconsistency Identification in Dynamic Ontologies Based on Model Checking

Gueffaz

Pittet

Rampacek

et al. 2012

Proceedings of the 8th International Conference on Web Information Systems and Technologies

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Abstract:The increasing use of ontologies and the cost of changes support the need to manage the evolution of ontologies. A common kind of error in ontology evolution is the logical contradiction declined as incoherences and inconsistences. In this paper, we propose a new approach to predict and identify the incoherences and inconsistences in the evolution of ontologies based on temporal logic and ontology design patterns. We implement the proposed approach using the NuSMV model checker. Based on these patterns, we propose an automated process to guide and monitor the implementation of change while ensuring the consistency of the evolved ontology.

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Section: Related Workmentioning

confidence: 99%

Inconsistency Identification in Dynamic Ontologies Based on Model Checking

Gueffaz

Pittet

Rampacek

et al. 2012

Proceedings of the 8th International Conference on Web Information Systems and Technologies

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“…An approach to address this problem is the creation of APIs that can be used across different platforms. This approach has been applied to integrate data from different social networks platforms [97]. …”

Section: Technical and Socioethical Challengesmentioning

confidence: 99%

Review of Extracting Information From the Social Web for Health Personalization

Fernández-Luque¹,

Karlsen²,

Bonander³

2011

J Med Internet Res

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In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires.This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization.

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“…Many researches have discussed the characteristics of an ontology evolution process (Klein, 2004;Stojanovic et al, 2002a;Stojanovic et al, 2002b) and several ontology evolution approaches have been proposed in the literature. Some focus on specific change management issues like capturing change requirements (Stojanovic, Stojanovic, Gonzalez, & Studer, 2003a;Cimiano & Völker, 2005;Bloehdorn, Haase, Sure, & Voelker, 2006), change detection and version logging (Klein, Fensel, Kiryakov, & Ognyanov, 2002a;Noy, Kunnatur, Klein, & Musen, 2004;Plessers & De Troyer, 2005;Eder & Wiggisser, 2007), formal change specification (Stojanovic, Stojanovic, & Volz, 2002c;Klein, 2004;Plessers De Troyer, & Casteleyn, 2007), change implementation (Stojanovic, Maedche, Stojanovic, & Studer, 2003b;Stojanovic, 2004;Flouris, 2006), consistency maintenance (Stojanovic, 2004 ;Haase & Stojanovic, 2005;Haase & Völker, 2005;Plessers & De Troyer, 2006), ontology versioning (Klein & Fensel, 2001;Klein et al, 2002a;Klein, 2004), and others propose a more or less global evolution process including change impact analysis and resolution as well as change propagation to dependant artifacts (objects, ontologies and applications referenced by the ontology) (Stojanovic, 2004;Klein, 2004;Bloehdorn et al, 2006). In this section, we present the main approaches and we analyze the functionalities that they support.…”

Section: Ontology Evolution Approachesmentioning

confidence: 99%

“…Consistency maintenance is also based on resolution principles presented in (Haase & Stojanovic, 2005). When confident axioms transformed from LOM to OWL lead to an inconsistent ontology, the inconsistency is localized.…”

Section: Learning Consistent Ontologiesmentioning

confidence: 99%

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Ontology Evolution

Djedidi

Aufaure²

Ontology Theory, Management and Design

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Ontologies evolve continuously throughout their lifecycle to respond to different change requirements. Several problems emanate from ontology evolution: capturing change requirements, change representation, change impact analysis and resolution, change validation, change traceability, change propagation to dependant artifacts, versioning, etc. The purpose of this chapter is to gather research and current developments to manage ontology evolution. The authors highlight ontology evolution issues and present a state-of-the-art of ontology evolution approach by describing issues raised and the ontology model considered (ontology representation language), and also the ontology engineering tools supporting ontology evolution and maintenance. Furthermore, they sum up the state-of-the-art review by a comparative study based on general characteristics, evolution functionalities supported, and specificities of the existing ontology evolution approaches. At the end of the chapter, the authors discuss future and emerging trends.

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The Semantic Web: Research and Applications

Cited by 14 publications

References 0 publications

Inconsistency Identification in Dynamic Ontologies Based on Model Checking

Inconsistency Identification in Dynamic Ontologies Based on Model Checking

Review of Extracting Information From the Social Web for Health Personalization

Ontology Evolution

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