Document retrieval using fuzzy related geographic ontologies

ABSTRACT

Many documents stored in digital libraries and document database include geographic references within their texts typically by means of place names. These geographic references can be associated to knowledge to help improve related retrieved documents. For example, in a meteorological document collection the climate knowledge can be associated to the geographic references allowing the retrieval of relevant climate or geographic related documents to an initial user query. In order to explore these issues this work describes a framework to encode a geographic knowledge base composed of multiple related ontologies whose relationships are expressed as fuzzy relations. Each ontology represents a distinct area of the knowledge domain related to geographic references. This knowledge organization is used in a fuzzy method to expand the user initial query. Each ontology can be represented independently as well as their relationships. The fuzzy query expansion method is tested with the Apache Lucene search engine improving the precision measure.

REFERENCES

• 1: Apache. Lucene overview. http://lucene.apache.org/java/docs/index.html.
• 2: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 3: J. Bhogal , A. Macfarlane , P. Smith, A review of ontology based query expansion, Information Processing and Management: an International Journal, v.43 n.4, p.866-886, July, 2007 [doi>10.1016/j.ipm.2006.09.003]
• 4: C. Bratsas, V. Koutkias, E. Kaimakamis, P. Bamidis, and N. Maglaveras. Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions. In EMBS 2007: Proceedings of the 29th IEEE Annual International Conference on Engineering in Medicine and Biology Society pages 3794--3797, Washington, DC, USA, 2007. IEEE Computer Society.
• 5: D. Buscaldi, P. Rosso, and P. P. García. Inferring geographical ontologies from multiple resources for geographical information retrieval. In GIR'06: Proceedings of the 3rd ACM workshop on Geographical information retrieval New York, NY, USA, 2006. ACM.
• 6: Nuno Cardoso , Mário J. Silva, Query expansion through geographical feature types, Proceedings of the 4th ACM workshop on Geographical information retrieval, November 09-09, 2007, Lisbon, Portugal [doi>10.1145/1316948.1316963]
• 7: M. S. Chaves, M. J. Silva,and B. Martins. A geographic knowledge base for semantic web applications.In SBBD pages 40--54, 2005.
• 8: M. D. Cock and C. Cornelis. Fuzzy rough set based web query expansion. In Proceedings of Rough Sets and Soft Computing in Intelligent Agent and Web Technology, International Workshop at WI-IAT pages 9--16,2005.
• 9: Embrapa. Bases de dados da pesquisa agropecuária. http://www.bdpa.cnptia.embrapa.br/.
• 10: Embrapa. Brazilian agriculture research corporation. http://www.embrapa.br/english.
• 11: G. Fu, C. B. Jones, and A. I. Abdelmoty. Building a geographical ontology for intelligent spatial search on the web. In Databases and Applications pages 167--172, 2005.
• 12: G. Fu, C. B. Jones, and A. I. Abdelmoty. Ontology-based spatial query expansion in information retrieval. Lecture Notes in Computer Science 3761/2005:1466--1482, 2005.
• 13: Asuncion Gomez-Perez , Oscar Corcho-Garcia , Mariano Fernandez-Lopez, Ontological Engineering, Springer-Verlag New York, Inc., Secaucus, NJ, 2003
• 14: IBGE. Mapa de climas. http://mapas.ibge.gov.br/clima/viewer.htm.
• 15: C. B. Jones, A. I. Abdelmoty, and G. Fu. Maintaining ontologies for geographical information retrieval on the web. In CoopIS/DOA/ODBASE pages 934--951, 2003.
• 16: Maria Angelica Leite , Ivan L. Ricarte, A Framework for Information Retrieval Based on Fuzzy Relations and Multiple Ontologies, Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence, October 14-17, 2008, Lisbon, Portugal [doi>10.1007/978-3-540-88309-8_30]
• 17: Miguel R. Luaces , Jose R. Parama , Oscar Pedreira , Diego Seco , Jose R. R. Viqueira, An Index Structure To Retrieve Documents With Geographic Information, Proceedings of the 18th International Conference on Database and Expert Systems Applications, p.64-68, September 03-07, 2007 [doi>10.1109/DEXA.2007.35]
• 18: B. Martins, M. J. Silva, S. Freitas, and A. P. Afonso. Handling locations in search engine queries. In GIR 2006.
• 19: Mike Nachtegael , Martine De Cock , Dietrich Van der Weken , Etienne E. Kerre, Fuzzy Relational Images in Computer Science, Revised Papers from the 6th International Conference and 1st Workshop of COST Action 274 TARSKI on Relational Methods in Computer Science, p.134-151, October 16-21, 2001
• 20: Yasushi Ogawa , Tetsuya Morita , Kiyohiko Kobayashi, A fuzzy document retrieval system using the keyword connection matrix and a learning method, Fuzzy Sets and Systems, v.39 n.2, p.163-179, Jan. 25, 1991 [doi>10.1016/0165-0114(91)90210-H]
• 21: W. Pedrycz and F. Gomide. An introduction to fuzzy sets: Analysis and Design MIT Press, Cambridge, Massachusetts, 1998.
• 22: R. Pereira, I. Ricarte, and F. Gomide. Fuzzy relational ontological model in information search systems. In Elie Sanchez. (Org.). Fuzzy Logic and The Semantic Web pages 395--412, Amsterdan, 2006.Elsevier B. V.
• 23: SISGA. Mapa do clima no brasil. http://campeche.inf.furb.br/sisga/educacao/ensino/mapaClima.php.
• 24: Wikipedia.Köppen climate classification. http://en.wikipedia.org/wiki/ Koppen climate classification.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation

General Terms:
Algorithms

Keywords:
fuzzy query expansion, geographic ontology, information retrieval