An Ontology-Driven Annotation of Data Tables

  • Gaëlle Hignette
  • Patrice Buche
  • Juliette Dibie-Barthélemy
  • Ollivier Haemmerlé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4832)


This paper deals with the integration of data extracted from the web into an existing data warehouse indexed by a domain ontology. We are specially interested in data tables extracted from scientific publications found on the web. We propose a way to annotate data tables from the web according to a given domain ontology. In this paper we present the different steps of our annotation process. The columns of a web data table are first segregated according to whether they represent numeric or symbolic data. Then, we annotate the numeric (resp.symbolic) columns with their corresponding numeric (resp. symbolic) type found in the ontology. Our approach combines different evidences from the column contents and from the column title to find the best corresponding type in the ontology. The relations represented by the web data table are recognized using both the table title and the types of the columns that were previously annotated. We give experimental results of our annotation process, our application domain being food microbiology.


ontology-driven data integration semantic annotation 


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  1. 1.
    Buche, P., Dervin, C., Haemmerlé, O., Thomopoulos, R.: Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules. IEEE T. Fuzzy Systems 13(3), 373–383 (2005)CrossRefGoogle Scholar
  2. 2.
    Zanibbi, R., Blostein, D., Cordy, J.R.: A survey of table recognition: Models, observations, transformations, and inferences. International Journal on Document Analysis and Recognition 7, 1–16 (2004)Google Scholar
  3. 3.
    Pivk, A., Cimiano, P., Sure, Y.: From tables to frames. In: Third International Semantic Web Conference, pp. 116–181 (2004)Google Scholar
  4. 4.
    Tenier, S., Toussaint, Y., Napoli, A., Polanco, X.: Instantiation of relations for semantic annotation. In: International Conference on Web Intelligence, pp. 463–472 (2006)Google Scholar
  5. 5.
    Embley, D.W., Tao, C., Liddle, S.W.: Automatically extracting ontologically specified data from html tables of unknown structure. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, pp. 322–337. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Freitag, D., Kushmerick, N.: Boosted wrapper induction. In: 17th National Conference on Artificial Intelligence, pp. 577–583 (2000)Google Scholar
  7. 7.
    Baumgartner, R., Flesca, S., Gottlob, G.: Visual web information extraction with Lixto. In: International Conference on Very Large Data Bases, pp. 119–128 (2001)Google Scholar
  8. 8.
    Gagliardi, H., Haemmerlé, O., Pernelle, N., Saïs, F.: An automatic ontology-based approach to enrich tables semantically. In: AAAI Context and Ontologies Workshop (2005)Google Scholar
  9. 9.
    Lin, D.: An information-theoretic definition of similarity. In: International Conference on Machine Learning, pp. 296–304 (1998)Google Scholar
  10. 10.
    Hignette, G., Buche, P., Dervin, C., Dibie-Barthélemy, J., Haemmerlé, O., Soler, L.: Fuzzy semantic approach for data integration applied to risk in food: an example about the cold chain. In: Proceedings of the 13th World Congress of Food Science and Technology, Food is Life (2006)Google Scholar
  11. 11.
    Van Rijsbergen, C.J.: Information Retrieval, 2nd edn., Dept. of Computer Science, University of Glasgow (1979)Google Scholar
  12. 12.
    Yangarber, R., Lin, W., Grishman, R.: Unsupervised learning of generalized names. In: International Conference on Computational Linguistics, pp. 1–7 (2002)Google Scholar
  13. 13.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  14. 14.
    Zadeh, L.: Fuzzy sets. Information and control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gaëlle Hignette
    • 1
  • Patrice Buche
    • 1
  • Juliette Dibie-Barthélemy
    • 1
  • Ollivier Haemmerlé
    • 2
  1. 1.UMR AgroParisTech/INRA MIA - INRA Unité Mét@risk, AgroParisTech, 16 rue Claude Bernard, F-75231 Paris Cedex 5France
  2. 2.IRIT - Université Toulouse le Mirail, Dpt. Mathématiques-Informatique, 5 allées Antonio Machado, F-31058 Toulouse Cedex 

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