Target Concept Selection by Property Overlap in Ontology Population

An ontology is widely used in many kinds of applications as a knowledge representation tool for domain knowledge. However, even though an ontology schema is well prepared by domain experts, it is tedious and cost-intensive to add instances into the ontology. The most confident and trust-worthy way to add instances into the ontology is to gather instances from tables in the related Web pages. In automatic populating of instances, the primary task is to find the most proper concept among all possible concepts within the ontology for a given table. This paper proposes a novel method for this problem by defining the similarity between the table and the concept using the overlap of their properties. According to a series of experiments, the proposed method achieves 76.98% of accuracy. This implies that the proposed method is a plausible way for automatic ontology population from Web tables.

• Seong-Bae Park
• Sang-Soo Kim
• Sewook Oh
• Zooyl Zeong
• Hojin Lee
• Seong Rae Park

• Ontology population
• domain knowledge consolidation
• target concept selection
• property overlap.

[1] J. Barrasa, O' . Corcho, and A. Go'mez-Pe'rez, "R2O, an Extensible
and Semantically Based Database-to-Ontology Mapping Language," In
Proceedings of the 2nd Workshop on Semantic Web and Databases,
2004.
[2] A. Budanitsky and G. Hirst, "Evaluating WordNet-based Measures of
Semantic Distance," Computational Linguistics, Vol. 32, No. 1, pp. 13-
47, 2006.
[3] S. Castano, S. Espinosa, A. Ferrara, V. Karkaletsis, A. Kaya, S. Melzer,
R. M¨oller, S. Montanelli, and G. Petasis, "Ontology Dynamics with
Multimedia Information: The BOEMIE Evolution Methodology," In
Proceedings of International Workshop on Ontology Dynamics, 2007.
[4] H. Davulcu, S. Vadrevu, S. Nagarajan, and I.V. Ramakrishnan, "OntoMiner:
Bootstrapping and Populating Ontologies from Domain-
Specific Web Sites," IEEE Intelligent Systems, Vol. 18, No. 5, pp. 24-33,
2003.
[5] S. Handschuh, R. Volz, and S. Staab, "Annotating for the Deep Web,"
IEEE Intelligent Systems, Vol. 18, No. 5, pp. 42-48, 2003.
[6] J. Jiang and D. Conrath, "Semantic Similarity based on Corpus Statistics
and Lexical Taxonomy," In Proceedings of the 10th International
Conference on Research in Computational Linguistics, pp. 19-33, 1997.
7] E. Sang, S. Canisius, A. Bosch, and T. Bogers, "Applying Spelling Error
Techniques for Improving Semantic Role Labelling," In Proceedings of
the 9th Conference on Computational Natural Language Learning pp.
229-232, 2005.
[8] T. Sugibuchi and Y. Tananka, "Interactive Web-Wrapper Construction
for Extracting Relational Information from Web Documents," In Proccedings
of the 14th International Conference on World Wide Web, pp.
968-969, 2005.