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Information Retrieval with a Simplified Conceptual Graph-Like Representation

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Advances in Artificial Intelligence (MICAI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6437))

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Abstract

We argue for that taking into account semantic relations between words in the text can improve information retrieval performance. We implemented the process of information retrieval with simplified Conceptual Graph-like structures and compare the results with those of the vector space model. Our semantic representation, combined with a small simplification of the vector space model, gives better results. In order to build Conceptual Graph-like representation, we have developed a grammar based on the dependency formalism and the standard defined for Conceptual Graphs (CG). We used noun pre-modifiers and noun post-modifiers, as well as verb frames, extracted from VerbNet, as a source of definition of semantic roles. VerbNet was chosen since its definitions of semantic roles have much in common with the CG standard. We experimented on a subset of the ImageClef 2008 collection of titles and annotations of medical images.

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References

  1. Amghar, T., Battistelli, D., Charnois, T.: Reasoning on aspectual-temporal information in French within conceptual graphs. In: 14th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2002, pp. 315ā€“322 (2002)

    Google ScholarĀ 

  2. Badia, A., Kantardzic, M.: Graph building as a mining activity: finding links in the small. In: Proceedings of the 3rd International Workshop on Link Discovery LinkKDD 2005, pp. 17ā€“24. ACM, New York (2005)

    Google ScholarĀ 

  3. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, Pearson Addison Wesley (1999)

    Google ScholarĀ 

  4. Barbu, E., Heroux, P., Adam, S., Trupin, E.: Clustering document images using a bag of symbols representation. In: Proceedings, Eighth International Conference on Document Analysis and Recognition, vol.Ā 2, pp. 1216ā€“1220 (2005)

    Google ScholarĀ 

  5. BarcelĆ³, G., Cendejas, E., Bolshakov, I., Sidorov, G.: AmbigĆ¼edad en nombres hispanos. Revista Signos. Estudios de LingĆ¼Ć­sticaĀ 42(70), 153ā€“169 (2009)

    Google ScholarĀ 

  6. BarriĆØre, C., BarriĆØre, N.C.: From a Childrenā€™s First Dictionary to a Lexical Knowledge Base of Conceptual Graphs. St. Leonards (NSW): Macquarie Library (1997)

    Google ScholarĀ 

  7. Barski, C.: TheĀ enigmaticĀ artĀ ofĀ knowledgeĀ representation, http://www.lisperati.com/tellstuff/ind-ex.html (accessed March 2010)

  8. Castro-SĆ”nchez, N.A., Sidorov, G.: Analysis of Definitions of Verbs in an Explanatory Dictionary for Automatic Extraction of Actants based on Detection of Patterns. LNCS, vol.Ā 6177, pp. 233ā€“239. Springer, Heidelberg (2010)

    Google ScholarĀ 

  9. Delugach, H.S.: Towards. Conceptual Structures Interoperability Using Common Logic Computer. In: Third Conceptual Structures Tool Interoperability Workshop. Science Department Univ. of Alabama in Huntsville (2008)

    Google ScholarĀ 

  10. Figuerola, G.C., Zazo, F.A., Berrocal, J.L.A.: CategorizaciĆ³n automĆ”tica de documentos en espaƱol: algunos resultados experimentales. Universidad de Salamanca, Facultad de DocumentaciĆ³n, Salamanca EspaƱa, 6ā€“16 (2000)

    Google ScholarĀ 

  11. Gelbukh, A., Sidorov, G., Galicia, S., Bolshakov, I.: Environment for Development of a Natural Language Syntactic Analyzer. In: Acta Academia, Moldova, pp. 206ā€“213 (2002)

    Google ScholarĀ 

  12. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of SciencesĀ 101(suppl. 1), 5228ā€“5235 (2004)

    ArticleĀ  Google ScholarĀ 

  13. Helbig, H.: Knowledge Representation and the Semantics of Natural Language. Springer, Heidelberg (2006)

    MATHĀ  Google ScholarĀ 

  14. Hensman, S.: Construction of Conceptual Graph representation of texts. In: Proceedings of Student Research Workshop at HLT-NAACL, Department of Computer Science, University College Dublin, Belfield, Dublin 4 (2004)

    Google ScholarĀ 

  15. Hensman, S., Dunnion, J.: Automatically building conceptual graphs using VerbNet and WordNet. In: 2004 International Symposium on Information and Communication Technologies, Las Vegas, Nevada, June 16-18. ACM International Conference Proceeding Series, vol.Ā 90, pp. 115ā€“120. Trinity College, Dublin (2004)

    Google ScholarĀ 

  16. Hensman, S., Dunnion, J.: Constructing conceptual graphs using linguistic resources. In: Husak, M., Mastorakis, N. (eds.) Proceedings of the 4th WSEAS International Conference on Telecommunications and Informatics, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, Prague, Czech Republic, March 13-15, pp. 1ā€“6 (2005)

    Google ScholarĀ 

  17. Hensman, S.: Construction of conceptual graph representation of texts. In: Proceedings of the Student Research Workshop at HLT-NAACL 2004, Boston, Massachusetts, May 02ā€“07. Human Language Technology Conference, pp. 49ā€“54. Association for Computational Linguistics, Morristown (2004)

    ChapterĀ  Google ScholarĀ 

  18. HernƔndez Cruz, M.: Generador de los grafos conceptuales a partir del texto en espaƱol. MSc thesis. Instituto PolitƩcnico Nacional, Mexico (2007)

    Google ScholarĀ 

  19. Kamaruddin, S., Bakar, A., Hamdan, A., Nor, F.: Conceptual graph formalism for financial text representation. In: International Symposium on Information Technology (2008)

    Google ScholarĀ 

  20. Kipper, K., Korhonen, A., Ryant, N., Palmer, M.: Extending VerbNet with Novel Verb Classes. In: 5th International Conf. on Language Resources and Evaluation, LREC 2006, Genoa, Italy (June 2006), http://verbs.colorado.edu/~mpalmer/projects/verbnet.html

  21. Kovacs, L., Baksa-Varga, E.: Dependency-based mapping between symbolic language and Extended Conceptual Graph. In: 6th International Symposium on Intelligent Systems and Informatics (2008)

    Google ScholarĀ 

  22. Medical Image Retrieval Challenge Evaluation P., http://ir.ohsu.edu/image

  23. Manning, C.D., Raghavan, P., SchĆ¼tze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008), http://www-nlp.stanford.edu/IR-book

    BookĀ  MATHĀ  Google ScholarĀ 

  24. National Library of Medicine, National of Institute of Health. United States Unified Medical Language System (UMLS), http://www.nlm.nih.gov/research/umls/about_umls.html (accessed April 2010)

  25. Peltonen, J., Sinkkonen, J., Kaski, S.: Discriminative clustering of text documents. In: 9th International Conference on Neural Information Processing, ICONIP 2002, pp. 1956ā€“1960 (2002)

    Google ScholarĀ 

  26. PĆ©rez-CoutiƱo, M., Montes-y-GĆ³mez, M., LĆ³pez-LĆ³pez, A.: Applying dependency trees and term density for answer selection reinforcement. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol.Ā 4730, pp. 424ā€“431. Springer, Heidelberg (2007)

    ChapterĀ  Google ScholarĀ 

  27. Porter, M.: An algorithm for suffix stripping. ProgramĀ 14(3), 130ā€“137 (1980), http://tartaus.org/~martin/PorterStemmer/

    ArticleĀ  Google ScholarĀ 

  28. Rassinoux, A.M., Baud, R.H., Scherrer, J.R.: A Multilingual Analyser of Medical Texts Conceptual Structures. In: Proceedings of 2nd International Conference on Conceptual Structures, ICCS 1994, College Park, Maryland, USA, August 16-20 (1994)

    Google ScholarĀ 

  29. Rassinoux, A.M., Baud, R.H., Lovis, C., Wagner, J.C., Scherrer, J.R.: Tuning Up Conceptual Graph Representation for Multilingual Natural Language Processing in Medicine Conceptual Structures: Theory, Tools, and Applications. In: Proceedings of 6th International Conference on Conceptual Structures, ICCS 1998, Montpellier, France (August 1998)

    Google ScholarĀ 

  30. Reddy, K.C., Reddy, C.S.K., Reddy, P.G.: Implementation of conceptual graphs using frames in lead. In: Ramani, S., Anjaneyulu, K.S.R., Chandrasekar, R. (eds.) KBCS 1989. LNCS, vol.Ā 444, pp. 213ā€“229. Springer, Heidelberg (1990)

    ChapterĀ  Google ScholarĀ 

  31. Rege, M., Dong, M., Fotouhi, F.: Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning. In: Proceedings Sixth International Conference Data Mining ICDM 2006, pp. 532ā€“541 (2006)

    Google ScholarĀ 

  32. Salton, G.: Relevance assessments and Retrieval system evaluation. Information Storage and Retrieval (1969)

    Google ScholarĀ 

  33. Schenker, A., Bunke, H., Last, M., Kandel, A.: A Graph-Based Framework for Web Document Mining. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol.Ā 3163, pp. 401ā€“412. Springer, Heidelberg (2004)

    ChapterĀ  Google ScholarĀ 

  34. Schenker, A., Bunke, H., Last, M., Kandel, A.: Graph-Theoretic Techniques for Web Content Mining. World Scientific Publishing, Singapore (2005)

    BookĀ  MATHĀ  Google ScholarĀ 

  35. Shafiei, M., Milios, E.: Latent Dirichlet Co-Clustering. In: Sixth International Conference on, Data Mining (CDM 2006), pp. 542ā€“551 (2006)

    Google ScholarĀ 

  36. Sleator, D., Temperley, D.: Parsing English with a link grammar. In: Third International Workshop on Parsing Technologies (1993)

    Google ScholarĀ 

  37. Sowa, J.F.: Conceptual Graphs. Handbook of Knowledge Representation (2008)

    Google ScholarĀ 

  38. Sowa, J.F., Way, E.C.: Implementing a semantic interpreter using conceptual graphs. IBM Journal of Research and DevelopmentĀ 30(1), 57ā€“69 (1986)

    ArticleĀ  Google ScholarĀ 

  39. TesniĆØre, L.: ƉlĆ©ments de syntaxe structurale, Klincksieck, Paris (1959)

    Google ScholarĀ 

  40. Williams, R.A.: Computational Effective Document Semantic Representation. In: Digital EcoSystems and Technologies Conference, DEST 2007. IEEE-IES (2007)

    Google ScholarĀ 

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OrdoƱez-Salinas, S., Gelbukh, A. (2010). Information Retrieval with a Simplified Conceptual Graph-Like Representation. In: Sidorov, G., HernƔndez Aguirre, A., Reyes Garcƭa, C.A. (eds) Advances in Artificial Intelligence. MICAI 2010. Lecture Notes in Computer Science(), vol 6437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16761-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-16761-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16760-7

  • Online ISBN: 978-3-642-16761-4

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