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Rough concept lattice based ontology similarity measure

Published: 30 May 2006 Publication History

Abstract

With the rapid development of the semantic web, it is likely that the number of ontologies will greatly increase during the next few years, which leads to the arising demand for rapid ontology mapping. In this paper, a novel similarity measure method based on rough set and concept lattice is proposed to realize ontology mapping tasks. A reference concept lattice is first constructed with the combination of two normalized contexts. Rough set theory is then employed to calculate the similarity measure of the two ontology nodes. With a specified threshold, the final result of ontology mapping can be obtained. Compared with other mapping algorithms, the proposed ontology mapping method is featural and structural, and the experiment shows the performance of the mapping method.

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cover image ACM Other conferences
InfoScale '06: Proceedings of the 1st international conference on Scalable information systems
May 2006
512 pages
ISBN:1595934286
DOI:10.1145/1146847
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2006

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InfoScale '06 Paper Acceptance Rate 33 of 91 submissions, 36%;
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  • (2022)Measuring the Similarity of Concept Maps According to Pedagogical CriteriaIEEE Access10.1109/ACCESS.2022.315666210(27655-27669)Online publication date: 2022
  • (2020)Semantic Web-Based Information Retrieval Models: A Systematic SurveyData Science and Analytics10.1007/978-981-15-5830-6_18(204-222)Online publication date: 28-May-2020
  • (2019)An Effective Approach of Measuring Disease Similarities Based on the DNN Regression ModelIntelligent Computing Theories and Application10.1007/978-3-030-26969-2_19(201-212)Online publication date: 24-Jul-2019
  • (2017)A framework for comparing concept maps2017 16th International Conference on Information Technology Based Higher Education and Training (ITHET)10.1109/ITHET.2017.8067818(1-6)Online publication date: Jul-2017
  • (2017)Using Fuzzy Ontology to Improve Similarity Assessment: Method and EvaluationInternational Journal of Intelligent Systems10.1002/int.2189532:11(1167-1186)Online publication date: 7-Mar-2017
  • (2014)Information retrieval using a novel concept similarity in formal concept analysis2014 International Conference on Information Science, Electronics and Electrical Engineering10.1109/InfoSEEE.2014.6947870(1249-1252)Online publication date: Apr-2014
  • (2013)A social dimensional cyber threat model with formal concept analysis and fact-proposition inferenceInternational Journal of Information and Computer Security10.1504/IJICS.2013.0582135:4(301-333)Online publication date: 1-Dec-2013
  • (2012)Semantic Web search based on rough sets and Fuzzy Formal Concept AnalysisKnowledge-Based Systems10.1016/j.knosys.2011.06.01826(40-47)Online publication date: 1-Feb-2012
  • (2012)A Similarity Measure Model Based on Rough Concept LatticeSoftware Engineering and Knowledge Engineering: Theory and Practice10.1007/978-3-642-03718-4_13(99-103)Online publication date: 15-Jan-2012
  • (2011)Research on Knowledge Transfer in Software Engineering by Concept Lattice IsomorphicAdvanced Research on Computer Science and Information Engineering10.1007/978-3-642-21402-8_31(191-197)Online publication date: 2011
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