Abstract
Semantic information retrieval is based on calculating similarity between concepts in a query and documents of a corpus. In this regard, similarity between concept pairs is determined by using an ontology or a meta-thesaurus. Although semantic similarities often convey reasonable meaning, there are cases where calculated semantic similarity fails to detect semantic relevancy between concepts. This problem often occurs when concepts in a corpus have statistical dependencies while they are not conceptually similar. In this paper a concept-based pseudo relevance feedback approach is introduced for discovering statistical relations between concepts in an ontology. Proposed approach consists of adding extra concepts to each query based on the concepts in top ranked documents which are rendered as relevant to that query. Results show that using conceptual relevance feedback would increase mean average precision by 19 percent compared to keyword based approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aronson, A., Rindflesch, T.: Query Expansion Using the UMLS Metathesaurus. In: Proceedings of AMIA Annual Fall Symposium, pp. 485–489 (1997)
Billerbeck, B., Zobel, J.: Techniques for Efficient Query Expansion. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 30–42. Springer, Heidelberg (2004)
Cui, H., Nie, J.J.: Query expansion by Mining user logs. IEEE Transaction on Knowledge and Data Engineering 15(4), 829–839 (2003)
Gauch, S.: A corpus analysis approach for automatic query expansion and its extension to multiple databases. ACM Transactions on Information Systems (TOIS) 17(3), 250–269 (1999)
Hersh, W., Price, S., Donohoe, L.: Assessing thesaurus-based query expansion using the UMLS metathesaurus. Journal of Americian Medical Informatics Association Annual Symp. (2000)
Houston, A.L., Chen, H.C.: Exploring the use of concept spaces to improve medical information retrieval. Decision Support Systems 30(2), 171–186 (2000)
Jalali, V., Borujerdi, M.R.M.: The Effect of Using Domain Specific Ontologies in Query Expansion in Medical Field. Innovations in Information Technology, 277–281 (2008)
Leroy, G., Chen, H.C.: Meeting Medical Terminology Needs: The Ontology-enhanced Medical Concept Mapper. IEEE Transactions on Information Technology in Biomedicine 5(4), 261–270 (2001)
Li, Y., Bandar, Z., McLean, D.: An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE 45 Transactions on Knowledge and Data Engineering 15(4), 871–882 (2003)
Mandala, R., Tokunaga, T., Tanaka, H.: Combining multiple evidence from different types of thesaurus for query expansion. In: SIGIR 1999: Proceedings of the 22nd Annual International ACM SIGIR, pp. 191–197 (1999)
Mao, W., Chu, W.: Free-text medical document retrieval via phrase-based vector space model. In: Proceedings of AMIA Annual Symp., vol. 61(1), pp. 76–92 (2002)
Rada, R., Bicknell, E.: Ranking documents with a thesaurus. Journal of the American Society for Information Science 40, 304–310 (1989)
Srinivasan, P.: Query Expansion and MEDLINE. Information Processing and Management 32(4), 431–443 (1996)
Voorhees, E.M.: Query expansion using lexical-semantic relations. SIGIR 94, 61–69 (1994)
Yoo, S., Choi, J.: Improving MEDLINE Document Retrieval Using Automatic Query Expansion. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 241–249. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Jalali, V., Borujerdi, M.R.M. (2009). Concept Based Pseudo Relevance Feedback in Biomedical Field. In: Lee, R., Ishii, N. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01203-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-01203-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01202-0
Online ISBN: 978-3-642-01203-7
eBook Packages: EngineeringEngineering (R0)