Information Retrieval with a Simplified Conceptual Graph-Like Representation

  • Sonia Ordoñez-Salinas
  • Alexander Gelbukh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6437)


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.


Information Retrieval Conceptual Graph Dependency Grammar 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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. 315–322 (2002)Google Scholar
  2. 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. 17–24. ACM, New York (2005)Google Scholar
  3. 3.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, Pearson Addison Wesley (1999)Google Scholar
  4. 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. 2, pp. 1216–1220 (2005)Google Scholar
  5. 5.
    Barceló, G., Cendejas, E., Bolshakov, I., Sidorov, G.: Ambigüedad en nombres hispanos. Revista Signos. Estudios de Lingüística 42(70), 153–169 (2009)Google Scholar
  6. 6.
    Barrière, C., Barrière, N.C.: From a Children’s First Dictionary to a Lexical Knowledge Base of Conceptual Graphs. St. Leonards (NSW): Macquarie Library (1997)Google Scholar
  7. 7.
    Barski, C.: The enigmatic art of knowledge representation, (accessed March 2010)
  8. 8.
    Castro-Sánchez, 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. 6177, pp. 233–239. Springer, Heidelberg (2010)Google Scholar
  9. 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 Scholar
  10. 10.
    Figuerola, G.C., Zazo, F.A., Berrocal, J.L.A.: Categorización automática de documentos en español: algunos resultados experimentales. Universidad de Salamanca, Facultad de Documentación, Salamanca España, 6–16 (2000)Google Scholar
  11. 11.
    Gelbukh, A., Sidorov, G., Galicia, S., Bolshakov, I.: Environment for Development of a Natural Language Syntactic Analyzer. In: Acta Academia, Moldova, pp. 206–213 (2002)Google Scholar
  12. 12.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences 101(suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  13. 13.
    Helbig, H.: Knowledge Representation and the Semantics of Natural Language. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  14. 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 Scholar
  15. 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. 90, pp. 115–120. Trinity College, Dublin (2004)Google Scholar
  16. 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. 1–6 (2005)Google Scholar
  17. 17.
    Hensman, S.: Construction of conceptual graph representation of texts. In: Proceedings of the Student Research Workshop at HLT-NAACL 2004, Boston, Massachusetts, May 02–07. Human Language Technology Conference, pp. 49–54. Association for Computational Linguistics, Morristown (2004)CrossRefGoogle Scholar
  18. 18.
    Hernández Cruz, M.: Generador de los grafos conceptuales a partir del texto en español. MSc thesis. Instituto Politécnico Nacional, Mexico (2007)Google Scholar
  19. 19.
    Kamaruddin, S., Bakar, A., Hamdan, A., Nor, F.: Conceptual graph formalism for financial text representation. In: International Symposium on Information Technology (2008)Google Scholar
  20. 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),
  21. 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 Scholar
  22. 22.
    Medical Image Retrieval Challenge Evaluation P.,
  23. 23.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008), CrossRefzbMATHGoogle Scholar
  24. 24.
    National Library of Medicine, National of Institute of Health. United States Unified Medical Language System (UMLS), (accessed April 2010)
  25. 25.
    Peltonen, J., Sinkkonen, J., Kaski, S.: Discriminative clustering of text documents. In: 9th International Conference on Neural Information Processing, ICONIP 2002, pp. 1956–1960 (2002)Google Scholar
  26. 26.
    Pérez-Coutiño, M., Montes-y-Gómez, M., López-López, 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. 4730, pp. 424–431. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980), CrossRefGoogle Scholar
  28. 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 Scholar
  29. 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 Scholar
  30. 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. 444, pp. 213–229. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  31. 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. 532–541 (2006)Google Scholar
  32. 32.
    Salton, G.: Relevance assessments and Retrieval system evaluation. Information Storage and Retrieval (1969)Google Scholar
  33. 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. 3163, pp. 401–412. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  34. 34.
    Schenker, A., Bunke, H., Last, M., Kandel, A.: Graph-Theoretic Techniques for Web Content Mining. World Scientific Publishing, Singapore (2005)CrossRefzbMATHGoogle Scholar
  35. 35.
    Shafiei, M., Milios, E.: Latent Dirichlet Co-Clustering. In: Sixth International Conference on, Data Mining (CDM 2006), pp. 542–551 (2006)Google Scholar
  36. 36.
    Sleator, D., Temperley, D.: Parsing English with a link grammar. In: Third International Workshop on Parsing Technologies (1993)Google Scholar
  37. 37.
    Sowa, J.F.: Conceptual Graphs. Handbook of Knowledge Representation (2008)Google Scholar
  38. 38.
    Sowa, J.F., Way, E.C.: Implementing a semantic interpreter using conceptual graphs. IBM Journal of Research and Development 30(1), 57–69 (1986)CrossRefGoogle Scholar
  39. 39.
    Tesnière, L.: Éléments de syntaxe structurale, Klincksieck, Paris (1959)Google Scholar
  40. 40.
    Williams, R.A.: Computational Effective Document Semantic Representation. In: Digital EcoSystems and Technologies Conference, DEST 2007. IEEE-IES (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sonia Ordoñez-Salinas
    • 1
  • Alexander Gelbukh
    • 2
  1. 1.Universidad Distrital F.J.C and Universidad NacionalColombia
  2. 2.Center for Computing Research (CIC)National Polytechnic Institute (IPN)Mexico

Personalised recommendations