Towards Category-Based Fuzzy Querying of Both Structured and Semi-Structured Imprecise Data

  • Patrice Buche
  • Ollivier Haemmerlé
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 7)


This work presents a part of a national project which aims at building a tool for the analysis of microbial risks in food products. As a first step, we propose a querying system using fuzzy values which must be compared to imprecise information stored in the database. This category-based unified querying system works in two steps. In the first one, the category of data concerned by the query is identified in order to build two queries which will be processed on two separate databases. In the second step, both previous queries scan simultaneously a relational database and a conceptual graph knowledge base, containing microbiological information; the results from the two scans are merged in a unique table format to be shown to the user. Fuzzy values and imprecise information are managed only in the relational database in this paper. It will be extended to conceptual graph knowledge base in a future paper.


Selection Attribute Conjunctive Query Query Graph Conceptual Graph Schema Graph 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Patrice Buche
    • 1
  • Ollivier Haemmerlé
    • 1
  1. 1.INA P-G, UER d’informatique/INRA BIA 16, rue Claude BernardFrance

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