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The Analysis for Ripple-Effect of Ontology Evolution Based on Graph

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Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9789))

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Abstract

Ontology is an important foundation for the Semantic Web and ontology-driven applications. While real network and application requirements are constantly changing, in order to adapt to these changes, the ontologies need to be updated in time. There are various types of semantic relationships, between the elements in ontology that the strength varies as well as their impacts of the process of evolution. To reflect them to the analysis of the ripple-effect of the ontology evolution, the common types of semantic relationships in ontology was extracted, and on this basis, proposed SRG (Semantic Relationship Graph) graph model in which the property-related semantic type substitutes the property and established the matrix and reachability matrix of the relationships among the elements in the ontology corresponding to this graph model. Then, the ripple-effect during the process of ontology evolution was analyzed via using matrix operations to calculate the comprehensive influence of the node in ontology. Experimental results show that the proposed method can provide accurate quantitative analysis for ripple-effect of ontology evolution under the premise of efficiency is possible.

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Acknowledgement

This work was partially supported by a grant from the NSF (Natural Science Foundation) of China under grant number 61272110, the Key Projects of National Social Science Foundation of China under grant number 11&ZD189, and it was partially supported by a grant from NSF of Hubei Prov. of China under grant number 2013CFB334. It was partially supported by NSF of educational agency of Hubei Prov. under grant number Q20101110, and the State Key Lab of Software Engineering Open Foundation of Wuhan University under grant number SKLSE2012-09-07.

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Qiuyao Lv, Yingping Zhang & Jinguang Gu

  2. Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province, Wuhan, 430065, China

    Qiuyao Lv, Yingping Zhang & Jinguang Gu

Authors
  1. Qiuyao Lv
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  2. Yingping Zhang
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  3. Jinguang Gu
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Corresponding author

Correspondence to Jinguang Gu .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing Univ. of Posts & Telecomm., Beijing, China

    Shangguang Wang

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© 2016 Springer International Publishing Switzerland

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Lv, Q., Zhang, Y., Gu, J. (2016). The Analysis for Ripple-Effect of Ontology Evolution Based on Graph. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9789. Springer, Cham. https://doi.org/10.1007/978-3-319-42089-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-42089-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42088-2

  • Online ISBN: 978-3-319-42089-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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