LRD: Latent Relation Discovery for Vector Space Expansion and Information Retrieval

  • Alexandre Gonçalves
  • Jianhan Zhu
  • Dawei Song
  • Victoria Uren
  • Roberto Pacheco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)


In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.


Mutual Information Information Retrieval Relation Strength Vector Space Model Textual Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Castillo, G., Sierra, G., McNaught, J.: An improved algorithm for semantic clustering. In: Proc. of 1st International Symposium on Information and Communication Technologies, Dublin, Ireland. ACM International Conference Proceeding Series, pp. 304–309 (2003)Google Scholar
  2. 2.
    Cunningham, H.: GATE: a General Architecture for Text Engineering. Computers and the Humanities 36(2), 223–254 (2002)CrossRefGoogle Scholar
  3. 3.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  4. 4.
    Ding, C.H.Q.: A probabilistic model for dimensionality reduction in information retrieval and filtering. In: Proc. of the 1st SIAM Computational Information Retrieval Workshop, Raleigh, NC (2000)Google Scholar
  5. 5.
    Hotho, A., Maedche, A., Staab, S.: Text clustering based on good aggregations. In: Proc. of the 2001 IEEE International Conference on Data Mining, pp. 607–608. IEEE Computer Society, San Jose (2001)CrossRefGoogle Scholar
  6. 6.
    Hotho, A., Stumme, G.: Conceptual clustering of text clusters. In: Proc. of the Fachgruppentreffen Maschinelles Lernen (FGML), Hannover, Germany, pp. 37–45 (2002)Google Scholar
  7. 7.
    Ikehara, S., Murakami, J., Kimoto, Y., Araki, T.: Vector space model based on semantic attributes of words. In: Proc. of the Pacific Association for Computational Linguistics (PACLING), Kitakyushu, Japan (2001)Google Scholar
  8. 8.
    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: An International Electronic and Print Journal 11, 95–130 (1999)zbMATHGoogle Scholar
  9. 9.
    Gonçalves, A., Uren, V., Kern, V., Pacheco, R.: Mining Knowledge from Textual Databases: An Approach using Ontology-based Context Vectors. In: Proc. of the International Conference on Artificial Intelligence and Applications (AIA 2005), Innsbruck, Austria, pp. 66–71 (2005)Google Scholar
  10. 10.
    Zhu, J., Uren, V., Motta, E.: ESpotter: Adaptive Named Entity Recognition for Web Browsing. In: Althoff, K.-D., Dengel, A.R., Bergmann, R., Nick, M., Roth-Berghofer, T.R. (eds.) WM 2005. LNCS (LNAI), vol. 3782, pp. 518–529. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Zhu, J., Gonçalves, A., Uren, V., Motta, E., Pacheco, R.: Mining Web Data for Competency Management. In: Proc. of Web Intelligence (WI 2005), France, pp. 94–100. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  12. 12.
    Church, K., Hanks, P.: Word association norms, mutual information, and lexicography. Computational Linguistics 16(1), 22–29 (1990)Google Scholar
  13. 13.
    Vechtomova, O., Robertson, S., Jones, S.: Query expansion with long-span collocates. Information Retrieval 6(2), 251–273 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alexandre Gonçalves
    • 1
  • Jianhan Zhu
    • 2
  • Dawei Song
    • 2
  • Victoria Uren
    • 2
  • Roberto Pacheco
    • 1
    • 3
  1. 1.Stela InstituteFlorianópolisBrazil
  2. 2.Knowledge Media InstituteThe Open UniversityMilton KeynesUnited Kingdom
  3. 3.Department of Computing and StatisticsFederal University of Santa CatarinaFlorianópolisBrazil

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