Using an Aligned Ontology to Process User Queries

  • Kleber Xavier Sampaio de Souza
  • Joseph Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)


Ontologies have been developed for a number of knowledge domains as diverse as clinical terminology, photo camera parts and micro-array gene expression data. However, processing user queries over a set of overlapping ontologies is not straightforward because they have often been created by independent groups of expertise, each of them adopting different configurations for ontology concepts. A project being carried out at the Brazilian Corporation of Agricultural Research has produced ontologies in sub-domains such as beef cattle, dairy cattle, sheep and beans, among others. This paper focuses on an alignment method for these ontologies based on Formal Concept Analysis, a data analysis technique founded on lattice theory, and a strategy for processing user queries.


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  1. 1.
    Fernandez-Breis, J., Martinez-Bejar, R.: A Cooperative Framework for Integrating Ontologies. International Journal of Human-Computer Studies (56), 665–720 (2002)Google Scholar
  2. 2.
    Chalupsky, H.: OntoMorph: A Translation System for Symbolic Knowledge. Principles of Knowledge Representation and Reasoning In: Proceedings of the Seventh International Conference on Knowledge Representation and Reasoning (KR 2000), Breckenridge, Colorado, USA (2000)Google Scholar
  3. 3.
    Chaudron, L., Maille, N., Boyer, M.: The cube lattice model and its applications. Applied Artificial Intelligence 17, 207–242 (2003)CrossRefGoogle Scholar
  4. 4.
    Cole, R., Eklund, P.: Application of Formal Concept Analysis to Information Retrieval using a Hierarchically Structured Thesaurus. In: Proceedings of the International Conference on Conceptual Graphs (ICCS 1996), University of New South Wales, Sydney, pp. 1-12 (1996)Google Scholar
  5. 5.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to Map Between Ontologies on the Semantic Web. In: Proceedings of the Eleventh International WWW Conference, Hawaii, US, pp. 662–673 (2002)Google Scholar
  6. 6.
    Dyvik, H.: Translations as Semantic Mirrors. In: Proceedings of Workshop W13: Multilinguality in the Lexicon II, The 13th Biennial European Conference on Artificial Intelligence (ECAI 1998), Brighton, UK, pp. 24–44 (1998)Google Scholar
  7. 7.
    FAO (Food and Agriculture Organization of the United Nations): AGROVOC: multilingual agricultural thesaurus. FAO. Rome, p. 612 (1995),
  8. 8.
    Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Gruber, T.R.: Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  10. 10.
    Groh, B., Strahringer, S., Wille, R.: TOSCANA-Systems Based on Thesauri. In: Mugnier, M.-L., Chein, M. (eds.) ICCS 1998. LNCS (LNAI), vol. 1453, pp. 127–138. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. The Knowledge Engineering Review 18(1), 131 (2003)CrossRefGoogle Scholar
  12. 12.
    Kalfoglou, Y., Schorlemmer, M.: Information-flow-based ontology mapping. In: Meersman, R., Tari, Z., et al. (eds.) CoopIS 2002, DOA 2002, and ODBASE 2002. LNCS, vol. 2519, pp. 1132–1151. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Kay, J., Holden, S.: Automatic Extraction of Ontologies from Teaching Document Metadata. In: Proceedings of the International Conference on Computers in Education (ICCE 2002), Auckland, New Zealand, pp. 1555–1556 (2002)Google Scholar
  14. 14.
    Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generating concept lattices. Journal Exp. Theor. Artif. Intell. 14(2-3), 189–216 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    McGuiness, D.L., Fikes, R., Rice, J., Wilder, S.: An Environment for Merging and Testing Large Ontologies. In: Proceedings of Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR 2000), Breckenridge, Colorado, USA (2000)Google Scholar
  16. 16.
    Mena, E., Kashyap, V., Illarramendi, A., Sheth, A.: Domain Specific Ontologies for Semantic Information Brokering on the Global Information Infrastructure. In: Proceedings of the 1st International Conference on Formal Ontology in Information Systems (FOIS 1998), pp. 269–283 (1998)Google Scholar
  17. 17.
    Mitra, P., Wiederhold, G.: An Algebra for the Composition of Ontologies. In: Proceedings of the Workshop on Knowledge Transformation for the Semantic Web. Lyon, France (2002)Google Scholar
  18. 18.
    Noy, N.F., Musen, M.A.: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In: Seventeenth National Conference on Artificial Intelligence (AAAI 2000), Austin, TX (2000)Google Scholar
  19. 19.
    Park, J., Hunting, S.: XML Topic Maps - Creating and Using Topic Maps for the Web, p. 544. Addison-Wesley, Reading (2002)Google Scholar
  20. 20.
    Priss, U.: Formalizing Botanical Taxonomies. In: Ganter, B., de Moor, A., Lex, W. (eds.) ICCS 2003. LNCS, vol. 2746, pp. 309–322. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Souza, K.X.S., Davis, J., Souza, M.I.F.: Organizing Information for the Agribusiness Sector: Embrapa’s Information Agency. In: Proceedings of, International Conference on Digital Archive Technologies. Taipei: Institute of Information Science - Academia Sinica, pp. 159–169 (2004)Google Scholar
  22. 22.
    Stumme, G., Madche, A.: FCA-Merge: Bottom-up merging of ontologies. In: Proceedings of the 7th Intl. Conf. on Artificial Intelligence (IJCAI 2001), Seattle, WA, pp. 225–230 (2001)Google Scholar
  23. 23.
    Stumme, G., Studer, R., Sure, Y.: Towards and Order-Theoretical Foundation for Maintaining and Merging Ontologies In: Proceedings of Referenzmodellierung 2000. Siegen, Germany (2000)Google Scholar
  24. 24.
    Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with Titanic. Data & Knowledge Engineering 42, 189–222 (2002)zbMATHCrossRefGoogle Scholar
  25. 25.
    Valtchev, P., Hacene, M.R., Huchard, M., Roume, C.: Extracting Formal Concepts out of Relational Data. In: Proceedings of the 4th Intl. Conference Journes de l’Informatique Messine (JIM 2003): Knowledge Discovery and Discrete Mathematics, Metz, France, pp. 37–49 (2003)Google Scholar
  26. 26.
    Wache, H., Vogele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hubner, S.: Ontology-based integration of information - a survey of existing approaches. In: Stuckenschmidt, H. (ed.) IJCAI-01 Workshop: Ontologies and Information Sharing, pp. 108–117 (2001)Google Scholar
  27. 27.
    Wille, R.: Restructuring Lattice Theory: an Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel, Dordrecht (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kleber Xavier Sampaio de Souza
    • 1
    • 2
    • 3
  • Joseph Davis
    • 2
  1. 1.Embrapa Information TechnologyCampinasBrazil
  2. 2.School of Information TechnologiesUniversity of SydneyAustralia
  3. 3.Research supported by Capes grant BEX0687/03-0 

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