Information retrieval by Semantic Similarity

Semantic Web and Information Systems

Volumn 2, Issue 3, 2006, Pages 55-73

  Hliaoutakis, Angelos ^a   Varelas, Giannis ^a   Voutsakis, Epimenidis ^a   Petrakis, Euripides G M ^a   Milios, Evangelos ^b  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Document retrieval systems; Information retrieval; Medical information systems; Ontologies; Semantic similarity

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER PROGRAMMING LANGUAGES; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; MATHEMATICAL MODELS; WEBSITES;

LEXICAL MATCHING; MEDICAL INFORMATION SYSTEMS; ONTOLOGIES; SEMANTIC SIMILARITY;

SEMANTICS;

EID: 33750042554     PISSN: 15526283     EISSN: 15526291     Source Type: Journal    
DOI: 10.4018/jswis.2006070104     Document Type: Article

References (40)

1. Arasu, A., Cho, J., Garcia-Molina, H., Paepke, A., & Raghavan, S. (2002). Searching the Web. ACM Transactions on Internet Technology, 7(1), 2-43.
2. Aslandongan, Y. A., & Yu, C. T. (2000). Evaluating strategies and systems for content-based indexing of person images on the Web. In Proceedings of the International Conference on Multimedia (pp. 313-321).
3. Attar, R.,& Fraenkel, A. S. (1977). Local feedback in full text retrieval systems.Journal of the ACM, 23(3), 397-417.
4. Collins-Thomson, K., & Callan, J. (2005). Query expansion using random walk models. In Proceedings of CIKM (pp. 704-711).
5. Hersh, W. R., Buckley, C., Leone, T. J., & Hickam, D. H. (1994). OHSUMED: An interactive retrieval evaluation and new large. In Proceedings of ACM SIGIR (pp. 192-201).
6. Hliaoutakis, A., Varelas, G., Petrakis, E. G. M., & Milios, E. (2006). MedSearch: A retrieval system for medical information based on semantic similarity. In Proceedings of ECDL (pp. 512-515).
7. Jiang, J. J., & Conrath, D. W. (1998). Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the International Conference on Research in Computational Linguistics.
8. Kherfi, M. L., Ziou, D., & Bernardi, A. (2004). Image retrieval from the World Wide Web: Issues, techniques, and systems. ACM Computing Surveys, 36(1), 35-67.
9. Leacok, C., & Chodorov, M. (1998). Combining local context and WordNet similarity for word sense identification. In C. Fellbaum (Ed.), WordNet: An electronic lexical database and some of its applications (chap. 11). Boston: MIT Press.
10. Li, Y., Bandar, Z. A., & McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 15(4), 871-882.
11. Lin, D. (1993). Principle-based parsing without overgeneration. In ACL (pp. 112-120).
12. Liu, S., Liu, F., Yu, C., & Meng, M. (2004). An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In Proceedings of ACM SIGIR (pp. 266-272).
13. Lord, P. W., Stevens, R. D., Brass, A., & Goble, C. A. (2003). Investigating semantic similarity measures across the gene ontology: The relationship between sequence and annotation. Bioninformatics, 19(10), 1275-1283.
14. Mandala, R., Takenobu, T., & Hozumi, T. (1998). The use of WordNet in information retrieval. In COLING/ACL (pp. 469-477).
15. Mihalcea, R., Corley, C., & Strapparava, C. (2006, July). Corpus-based and knowledge-based measures of text semantic similarity. In Proceedings of the American Association for Artificial Intelligence (AAAI 2006). Boston.
16. Miller, G., & Charles, W. (1991). Contextual correlates of semantic similarity. Language and Cognitive Processes, 6(1), 1-28.
17. Patwardhan, S., Banerjee, S., & Petersen, T. (2003, February). Using measures of semantic relatedness for word sense disambiguation. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, Mexico (pp. 241-257).
18. Petrakis, E., Kontis, K., Voutakis, E., & Milios, E. (2005). Relevance feedback methods for logo and trademark image retrieval on the Web. In Proceedings of ACM SAC, IAR (pp. 23-27).
19. Possas, B., Ziviani, N., Meira, W., & Neto, B. R. (2005). Set-based vector model: An efficient approach for correlation-based ranking. ACM Transactions on Information Systems, 23(4), 397-429.
20. Qiu, Y., & Frei, H. P. (1993). Concept-based query expansion. In Proceedings of SIGIR (pp. 160-169).
21. Rada, R., Mill, E., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transaction on Systems, Man, and Cybernetics, 19(1), 17-30.
22. Resnik, O. (1999). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity and natural language. Journal of Artificial Intelligence Research, 11, 95-130.
23. Richardson, R., & Smeaton, A. (1995). Using WordNet in a knowledge-based approach to information retrieval (Working Paper: CA-0395). Dublin, Ireland: School of Computer Applications.
24. Richardson, R., Smeaton, A., & Murphy, J. (1994). Using WordNet as a knowledge base for measuring semantic similarity between words (Working paper CA-1294). Dublin, Ireland: School of Computer Applications, Dublin City University.
25. Rochio, J. J. (1971). Relevance feedback in information retrieval. In G. Salton (Ed.), The SMART retrieval system - Experiments in automatic document processing (pp. 313-323). Englewood Cliffs, NJ: Prentice Hall.
26. Rodriguez, M. A., & Egenhofer, M. J. (2003). Determining semantic similarity among entity classes from different ontologies. IEEE Trans. on Knowledge and Data Engineering, 75(2), 442-456.
27. Rui, Y., Huang, T. S., Ortega, M., & Mechrota, S. (1998). Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transaction on Circ. and Syst. for Video Technology, 8(5), 644-655.
28. Salton, G. (1989). Automatic text processing: the transformation analysis and retrieval of information by computer. Reading, MA: Addison-Wesley.
29. Seco, N., Veale, T., & Hayes, J. (2004). An intrinsic information content metric for semantic similarity in WordNet. Ireland: Dept. of Computer Science, University College Dublin.
30. Shen, H. T., Ooi, B. C., & Tan, K. L. (2000). Giving meanings to WWW images. In Proceedings of the International Conference on Multimedia (pp. 39-47).
31. Smeulders, A. W. M, Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1349-1380.
32. Smith, J. R., & Chang, S. F. (1997). Visually searching the Web for content. IEEE Multimedia, 4(3), 12-20.
33. Taycher, L., Cascia, M. L. & Sclaroff, S. (1997). Image digestion and relevance feedback in the ImageRover WWW search engine. In Proceedings of the International Conference on Visual Information Systems (pp. 85-94).
34. Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327-352.
35. Varelas, G. (2005). Semantic similarity methods in WordNet and their application to information retrieval on the Web (TR-TUC-ISL-01-2005). Retrieved from http://www.intelligence.tuc.gr/publications/Varelas.pdf
36. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E. G. M., & Milios, E. (2005). Semantic similarity methods in WordNet and their application to information retrieval on the Web. In WIDM (pp. 10-16).
37. Voorhees, E. M. (1994). Query expansion using lexical-semantic relations. In Proceedings of the ACM SIGIR (pp. 61-69).
38. Voutsakis, E., Petrakis, E., & Milios, E. (2005). Weighted link analysis for logo and trademark image retrieval on the Web. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI) (pp. 581-585).
39. Wu, Z., & Palmer, M. (1994). Verb semantics and lexical selection. In ACL (pp. 133-138).
40. Yates, R. B., & Neto, B. R. (1999). Modern information retrieval. Boston: Addison Wesley Longman.