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Aligning ontologies in multiple agents' society

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A framework for aligning ontologies within the community of agents using different semantic similarity measures is proposed. The approach presented postulates the use of subjective logic for estimating concepts' correspondence and allows relating concepts from many ontologies. By means of subjective logic's consensus and recommendation operators the agents may exchange information about similarity of concepts and have possibility of relating concepts from non-aligned ontologies. Different strategies of achieving semantic interoperability within the agents' society are also discussed.
Czasopismo
Rocznik
Strony
129--137
Opis fizyczny
Bibliogr. 16 poz., rys.
Twórcy
Bibliografia
  • [1] Andrea M., Egenhofer M., Determining Semantic Similarity Among Entity Classes from Different Ontologies, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, 2003, 442-456.
  • [2] Bailin S. C., Truszkowski W., Ontology Negotiation between Agents Supporting Intelligent Information Management, 5th International Conference on Autonomous Agents, Workshop on Ontologies in Agent Systems, ACM Press, 2001, 13-20.
  • [3] Fensel D. et al.. Ontology Management: Survey, Requirements and Directions, 1ST Project IST-1999-10132, Vrije Universiteit Amsterdam, 2001.
  • [4] Hendler J., Agents and the Semantic Web, IEEE Int. Systems 16(2), 2001, 30-37.
  • [5] Hameed A. et al.. Detecting Mismatches among Experts' Ontologies Acquired through Knowledge Elicitation, 21th Int. Conference on Knowledge Based Systems and Applied Artificial Intelligence ES2001, Cambridge, UK, 2001, 9-24.
  • [6] Josang A., A Logic for Uncertain Probabilities, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(3), 2001, 279-311.
  • [7] Josang A., A Metric for Trusted Systems, Proceedings of the 21st National Security Conference, NSA, 1998, 68-77.
  • [8] Kalfoglou Y., Schorlemmer M., Ontology Mapping: the State of the Art, The Knowledge Engineering Review 18(1), 2003, 1-31.
  • [9] Klein M., Combining and Relating Ontologies: an Analysis of Problems and Solutions, IJCAl'2001, Amsterdam, 53-62.
  • [10] Lin D., An Information-Theoretic Definition of Similarity, International Conference on Machine Learning, ICML'98, Madison, USA, 1998, 296-304.
  • [11] McGuinness D. L., Fikes R., Rice J., Wilder S., An Environment for Merging and Testing Urge Ontologies, 7th International Conference on Principles of Knowledge Representation and Reasoning. Breckenridge, Colorado, 2001,203-237.
  • [12] Maedche A., Zacharias v., Clustering Ontology-Based Metadata in the Semantic Web, PKDD, lnai 2431, Springer-Verlag, 2002, 348-360.
  • [13] Nov N. F., Musen M. A., PROMPT. Algorithm for Automated Ontology Merging and Alignment, 17th National Conf. on Artificial Intelligence, USA. 2002, 450-455.
  • [14] Silva N., Rocha J., Semantic Web Complex Ontology Mapping, lEEE/WIC International Conference on Web Intelligence, Porto, Portugal, 2003, 82-88.
  • [15] Resnik P., Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language, Journal of Artificial Intelligence Research, Volume 11,1999,95-130.
  • [16] Shaw M. L. G., Gaines B. R., Comparing Conceptual Structures: Consensus, Conflict, Correspondence and Contrast, Knowledge Acquisition, 1(4), 1989, 341-363.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0008-0076
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