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2015 Volume 30
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RESEARCH ARTICLE   Open Access    

Ontology evolution: a process-centric survey

  • 1.

    Olayan School of Business

  • 2.

    FORTH

  • 3.

    Knowledge Media Institute

  • 4.

    Department of New Media Technology

More Information
  • Abstract: Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.
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  • Cite this article

    Fouad Zablith, Grigoris Antoniou, Mathieu d'Aquin, Giorgos Flouris, Haridimos Kondylakis, Enrico Motta, Dimitris Plexousakis, Marta Sabou. 2015. Ontology evolution: a process-centric survey. The Knowledge Engineering Review 30(1)45−75, doi: 10.1017/S0269888913000349
    Fouad Zablith, Grigoris Antoniou, Mathieu d'Aquin, Giorgos Flouris, Haridimos Kondylakis, Enrico Motta, Dimitris Plexousakis, Marta Sabou. 2015. Ontology evolution: a process-centric survey. The Knowledge Engineering Review 30(1)45−75, doi: 10.1017/S0269888913000349
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RESEARCH ARTICLE   Open Access    

Ontology evolution: a process-centric survey

  • 1.

    Olayan School of Business

  • 2.

    FORTH

  • 3.

    Knowledge Media Institute

  • 4.

    Department of New Media Technology

  • Published online: 28 August 2013
The Knowledge Engineering Review  30,  2015, 30(1): 45−75  |  Cite this article

Abstract: Abstract: Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

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    Acknowledgments

    • We would like to thank Nathalie Aussenac-Gilles, Philippe Laublet and Jeff Z. Pan for their invaluable time and feedback for producing earlier versions of this paper. This work was partially supported by the PlanetData NoE (FP7:ICT-2009.3.4, 257641).

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    • Copyright © Cambridge University Press 2013 2013Cambridge University Press
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    Cite this article
    Fouad Zablith, Grigoris Antoniou, Mathieu d'Aquin, Giorgos Flouris, Haridimos Kondylakis, Enrico Motta, Dimitris Plexousakis, Marta Sabou. 2015. Ontology evolution: a process-centric survey. The Knowledge Engineering Review 30(1)45−75, doi: 10.1017/S0269888913000349
    Fouad Zablith, Grigoris Antoniou, Mathieu d'Aquin, Giorgos Flouris, Haridimos Kondylakis, Enrico Motta, Dimitris Plexousakis, Marta Sabou. 2015. Ontology evolution: a process-centric survey. The Knowledge Engineering Review 30(1)45−75, doi: 10.1017/S0269888913000349
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