IIM  Vol.3 No.4 , July 2011
A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text
ABSTRACT
Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.

Cite this paper
nullI. Serra and R. Girardi, "A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text," Intelligent Information Management, Vol. 3 No. 4, 2011, pp. 119-124. doi: 10.4236/iim.2011.34014.
References
[1]   A. Maedche and S. Staab, “Mining Non-taxonomic Conceptual Relations from Text,” Proceedings of Knowledge Engineering and Knowledge Manageme Methods, Models and Tools: 12th International Conference, Berlin Springer, 2000, pp. 189-202.

[2]   D. Sanchez and A. Moreno, “Learn-ing Non-Taxonomic Relationships from Web Documents for Domain Ontology Construction,” Data and Knowledge Engineering, Vol. 64, No. 3, 2008, pp. 600-623.doi:10.1016/j.datak.2007.10.001

[3]   J. Allen, “Natural Language Understanding,” Redwood City, CA: The Benja-min/Cummings Publishing Company, 1995.

[4]   J. Lehmann and P. Hitzler, “A refinement Operator Based Learning Algorithm for the ALC Description Logic,” Proceedings of International Conference on Inductive Logic Programming, Springer-verlag, Corvallis Berlin, 2007, pp. 147-160.

[5]   J. Villaverde, A. Persson, D. Godoy and A. Amandi, “Supporting the Discovery and Labeling of Nontaxonomic Relationships in Ontology Learning,” Expert System Applications, Vol. 36, No. 7, 2009, pp. 10288-10294. doi:10.1016/j.eswa.2009.01.048

[6]   K. Bontcheva and H. Cunningham, “The Semantic Web: A New Opportu-nity and Challenge for Human Language Technology,” Proceedings of the Workshop on Human Language Technology for the Semantic Web and Web Services, Sanibel Island, 2003.

[7]   K. Dellschaft and S. Staab, “On How to Perform a Gold Standard Based Evaluation of Ontology Learning,” Proceedings of the 5th International Semantic Web Conference, Athens Springer, 2006, pp. 228-241.

[8]   L. Marinho and K. Buza, “Schmidt-Thieme, L. Folksonomy-based Collabulary Learning,” Proceedings of Interna-tional Semantic Web Conference, Karlsruhe Berlin: Springer-Verlag, 2008, pp. 261-276.

[9]   N. Guarino, C. Masolo and C. Vetere, “Ontoseek: Content-Based Access to the Web,” IEEE Intelligent Systems, Vol. 14, No. 3, 1999, pp. 70-80.doi:10.1109/5254.769887

[10]   P. Buitelaar, P. Cimiano and P. Magnini, “Ontology Learning from Text: Methods, Evalua-tion and Applications,” IOS Press, Amsterdam, 2006.

[11]   P. Cimiano, J. Volker and R. Studer, “Ontologies on Demand?—A Descrip-tion of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text,” Wissenschaft and Praxis, Vol. 57, No. 6-7, 2006, pp. 315-320.

[12]   R. Dale, H. Moisl and H. L. Somers, “Cyclic Redundancy Check,” VDM Publishing House Ltd, Saarbrücken, 2000.

[13]   R. Girardi, “Guiding Ontology Learning and Population by Knowledge System Goals,” Proceedings of In-ternational Conference on Knowledge Engineering and On-tology Development, Education INSTIIC, Valence, 2010, pp. 480-484.

[14]   R. Srikant and R. Agrawal, “Mining General-ized Association Rules,” Future Generation Computer Systerms, Vol. 13, No. 2-3, 1997, pp. 161-180. doi:10.1006/ijhc.1995.1081

[15]   T. R. Gruber, “Toward Principles for the Design of Ontologies Used for Knowledge Sharing,” International Journal of Human-Computer Studies, Vol. 43, 1995, pp. 907-928.

[16]   V. Alexiev, M. Breu, J. De Bruijn, D. Fensel, R. Lara and H. Lausen, “Information Inte-gration with Ontologies: Experiences from an Industrial Showcase,” Wiley, Neu-Ulm, 2005.

 
 
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