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Speranza, M. & Sprugnoli, R. (2005). From Text to Knowledge for the Semantic Web: the ONTOTEXT Project. SWAP 2005, Semantic Web Applications and Perspectives, Trento. McCarthy, J. (1993). Notes on formalization context. Proceedings of the 13th IJCAI, Vol. 1, pp. 555-560. , Jin, Y. & White, P. (2005). Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE. 43rd Annual Meeting of the Association for Computational Linguistics (ACL2005), Ann Arbour, Michigan, pp. 491-498.
Generality Degree of the Extracted Concepts: Another element which affects the concept’s quality is the level of generality for a concept. In order to evaluate the generality degree of the concepts, we focus only on concepts extracted from the Table 2. The detail of the extracted concepts Advisable clusters Improper clusters Unknown clusters Kmeans Algorithm 53. 2 % 26. 28 % 20. 81% Contextual Hierarchy Driven Ontology Learning advisable clusters. We based our manual evaluation on the OMT thesaurus (1999).
Aufaure, M-A. & Bennacer, N. (2007). Analyses and Fundamental ideas for a Relation Extraction Approach. The IEEE proceedings of the Workshop on Data Mining and Business Intelligence in conjunction with the IEEE 23rd International Conference on Data Engineering (ICDE’07), Turkey. , Wives, L. P. (2000). Concept-based knowledge discovery in texts extracted from the web. In SIGKDD Explorations. Volume 2, Issue 1, Page 29. , Speranza, M. & Sprugnoli, R. (2005). From Text to Knowledge for the Semantic Web: the ONTOTEXT Project.