Computing knowledge-based semantic similarity from the web: An application to the biomedical domain

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5914 LNAI, Issue , 2009, Pages 17-28

  Sánchez, David ^a   Batet, Montserrat ^a   Valls, Aida ^a  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

Author keywords

Biomedicine; Information content; Ontologies; Semantic similarity; SNOMED; UMLS; Web

Indexed keywords

BIOMEDICAL DOMAIN; BIOMEDICAL FIELDS; BIOMEDICINE; CLINICAL DATA; COMMON PROBLEMS; DOMAIN ONTOLOGIES; INFORMATION CONTENTS; KNOWLEDGE DOMAINS; SEMANTIC ANALYSIS; SEMANTIC SIMILARITY; SIMILARITY MEASURE;

KNOWLEDGE BASED SYSTEMS; LINGUISTICS; ONTOLOGY; QUERY LANGUAGES; SEMANTICS;

KNOWLEDGE ENGINEERING;

EID: 77249126946     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10488-6_6     Document Type: Conference Paper

References (26)

1. Bollegala, D., Matsuo, Y., Ishizuka, M.: WebSim: A Web-based Semantic Similarity Measure. In: The 21st Annual Conference of the Japanese Society for Artificial Intelligence, pp. 757-766 (2007)
2. Brill, E.: Processing Natural Language without Natural Language Processing. In: Proceedings of the 4th International Conference on Computational Linguistics and Intelligent Text Processing, Mexico City, Mexico, pp. 360-369 (2003)
3. Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of semantic distance. Computational Linguistics 32(1), 13-47 (2006)
4. Burgun, A., Bodenreider, O.: Comparing terms, concepts and semantic classes in WordNet and the Unified Medical Language System. In: Proceedings of the NAACL 2001 Workshop: WordNet and other lexical resources: Applications, extensions and customizations, Pittsburgh, PA, pp. 77-82 (2001)
5. Caviedes, J., Cimino, J.: Towards the development of a conceptual distance metric for the UMLS. Journal of Biomedical Informatics 37, 77-85 (2004)
6. Church, K.W., Gale, W., Hanks, P., Hindle, D.: Using Statistics in Lexical Analysis. In: Proceedings of Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, pp. 115-164 (1991)
7. Cilibrasi, R., Vitanyi, P.M.B.: The Google Similarity Distance. IEEE Transaction on Know-ledge and Data Engineering 19(3), 370-383 (2006)
8. Cimiano, P.: Ontology Learning and Population from Text. In: Algorithms, Evaluation and Applications. Springer, Heidelberg (2006)
9. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
10. Iosif, E., Potamianos, A.: Unsupervised Semantic Similarity Computation using Web Search Engines. In: Proceedings of the International Conference on Web Intelligence, pp. 381-387 (2007)
11. Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the International Conference on Research in Computational Linguistics, Japan, pp. 19-33 (1997)
12. Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum (ed.) WordNet: An electronic lexical database, pp. 265-283. MIT Press, Cambridge (1998)
13. Lee, J.H., Kim, M.H., Lee, Y.J.: Information retrieval based on conceptual distance in is-a hierarchies. Journal of Documentation 49(2), 188-207 (1993)
14. Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conf. on Machine Learning, pp. 296-304. Morgan Kaufmann, San Francisco (1998)
15. Lord, P., Stevens, R., Brass, A., Goble, C.: Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275-1283 (2003)
16. Miller, G., Leacock, C., Tengi, R., Bunker, R.T.: A Semantic Concordance. In: Proceedings of ARPA Workshop on Human Language Technology, Morristown, USA, pp. 303-308. Association for Computational Linguistics (1993)
17. Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1-28 (1991)
18. Neugyn, H.A., Al-Mubaid, H.: New Ontology-based Semantic Similarity Measure for the Biomedical Domain. In: IEEE Conference on Granular Computing, Atlanta, GA, USA, pp. 623-628 (2006)
19. Pedersen, T., Pakhomov, S., Patwardhan, S., Chute, C.: Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics 40, 288-299 (2007)
20. Rada, R., Mili, H., Bichnell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 9(1), 17-30 (1989)
21. 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 11, 95-130 (1999)
22. Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of 14th International Joint Conference on Artificial Intelligence, pp. 448-453 (1995)
23. Sánchez, D., Moreno, A.: Learning non-taxonomic relationships from web documents for domain ontology construction. Data Knowledge Engineering 63(3), 600-623 (2008)
24. Turney, P.D.: Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491-499. Springer, Heidelberg (2001)
25. Wilbu, W., Yang, Y.: An analysis of statistical term strength and its use in the indexing and retrieval of molecular biology texts. Computers in Biology and Medicine 26, 209-222 (1996)
26. Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32nd annual Meeting of the Association for Computational Linguistics, New Mexico, USA, pp. 133-138 (1994)