We present an information system developed to help assessing the microbiological risk in food. That information system contains experimental results in microbiology, mainly extracted from scientific publications. The increasing amount of the experimental results available and the difficulty to integrate them into a classic relational database schema led us to design a system composed of two distinct subsystems queried through a common interface. The first subsystem is a classic relational database. The second subsystem is a database containing weakly-structured pieces of information expressed in terms of conceptual graphs. The data stored in both bases can be fuzzy ones in order to take into account the specificities of the biological information. The uniform query language used on both relational database and conceptual graph database allows the users to express preferences by using fuzzy sets in their queries. The MIEL system is now operational and used by the microbiologists involved in the Sym’Previus French project.
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Ballows, A., Truper, H., Dworkin, M., Harder, W., & Schleifer, K. (Eds.) (1992) The prokaryotes, a handbook on the biology of bacteria: Ecophysiology, isolation, identification, applications (2nd ed.). Berlin Heidelberg New York: Springer.
Bosc, P., & Pivert, O. (1995). SQLf: A relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems, 3(1), 1–17.
Buche, P., Dervin, C., Haemmerlé, O., Surleau, E., & Thomopoulos, R. (2003). Carrying out the microbial risk in food products using the MIEL software: A new tool to query incomplete, imprecise and heterogeneously structured experimental data stored in a relational database. In Proceedings of the International Conference on Predictive Modelling in Food, ICPMF’03 Quimper, France (pp. 58–60). Berlin Heidelberg NewYork: Springer.
Buche, P., Dervin, C., Haemmerlé, O., & Thomopoulos, R. (2005). Fuzzy querying on incomplete, imprecise and heterogeneously structured data in the relational model using ontologies and rules. IEEE Transactions on Fuzzy Systems, 13(3), 373–383.
Buche, P., & Haemmerlé, O. (2000) Towards a unified querying system of both structured and semi-structured imprecise data using fuzzy views. In Proceedings of the 8th international conference on conceptual structures, Lecture notes in artificial intelligence #1867 Darmstadt, Germany (pp. 207–220). Berlin Heidelberg New York: Springer.
Cao, T. (1999). Foundations of order-sorted fuzzy set logic programming in predicate logic and conceptual graphs. Ph.D. thesis, University of Queensland, Australia.
Dubois, D., & Prade, H. (1988). Possibility theory—An approach to computerized processing of uncertainty. New York: Plenum.
Dubois, D., & Prade, H. (1995). Tolerant fuzzy pattern matching: An introduction. In P. Bosc & J. Kacprzyk (Eds.), Fuzziness in Database Management Systems (pp. 42–58). Berlin Heidelberg New York: Springer.
Dubois, D., Prade, H., & Rossazza, J. (1991). Vagueness, typicality and uncertainty in class hierarchies. International Journal of Intelligent Systems, 6, 167–183.
Galindo, J., Cubero, J., Pons, O., & Medina, J. (1998). A server for fuzzy SQL queries. In Proceedings of the 1998 workshop FQAS’98 (Flexible query-answering systems), Roskilde, Denmark (pp. 161–171). Berlin Heidelberg New York: Springer.
Genesereth, M., Keller, A., & Duschka, O. (1997). Infomaster: An information integration system. In Proceedings of SIGMOD 97 (pp. 539–542). New York: ACM.
Genest, D., & Salvat, E. (1998). A platform allowing typed nested graphs: how CoGITo became CoGITaNT. In Proceedings of the 6th international conference on conceptual structures (ICCS’1998), Lecture notes in artificial intelligence #1453, Montpellier, France (pp. 154–161). Berlin Heidelberg New York: Springer.
Ginsburg, S., & Hull, R. (1983). Order dependency in the relational model. Theoretical Computer Science, (26), 149–195.
Goasdoué, F., Lattes, V., & Rousset, M.-C. (2000). The use of CARIN language and algorithms for information integration: The PICSEL system. International Journal of Cooperative Information Systems, 4(9), 383–401.
Ireland, J., & Moller, A. (2000) Review of international food classification and description. Journal of Food Composition and Analysis, 13(4), 529–538.
Leporq, B., Membré, J., Dervin, C., Buche, P., & Guyonnet, J. (2005). The “Sym’Previus” software, a tool to support decisions to the foodstuff safety. International Journal of Food Microbiology, 100(1–3), 231–237.
Morton, S. (1987). Conceptual graphs and fuzziness in artificial intelligence. Ph.D. thesis, University of Bristol, UK.
Mugnier,M.,&Chein,M. (1992). Polynomial algorithms for projection and matching. In Proceedings of the 7th annual workshop on conceptual graphs, Lecture notes in artificial intelligence #754, Las Cruces, New Mexico (pp. 239–251). Berlin Heidelberg New York: Springer.
Mugnier, M., & Chein, M. (1996). Représenter des connaissances et raisonner avec des graphes. Revue d’intelligence Artificielle, 10(1), 7–56.
Ordille, J., Levy, A., & Rajaraman, A. (1996). Querying heterogeneous information sources using source descriptions. In Proceedings of the international conference on very large data bases (pp. 251–262). San Francisco, California: Morgan Kaufmann.
Prade, H., & Testemale, C. (1984). Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries. Information Sciences,34, 115–143.
Sebastiani, F. (1994). Aprobabilistic terminological logic for modelling information retrieval. In Proceedings of the 17th annual international ACM-SIGIR conference on research and development in information retrieval, Dublin, Ireland (pp. 122–130). Berlin Heidelberg New York: Springer.
Sowa, J. (1984). Conceptual structures—Information processing in mind and machine. Reading, Massachussetts: Addison-Welsey.
Thomopoulos, R., Buche, P., & Haemmerlé, O. (2003a). Different kinds of comparisons between fuzzy conceptual graphs. In Proceedings of the 11th international conference on conceptual structures, ICCS’2003, Lecture notes in artificial intelligence #2746, Dresden, Germany (pp. 54–68). Berlin Heidelberg New York: Springer.
Thomopoulos, R., Buche, P., & Haemmerlé, O. (2003b). Representation of weakly structured imprecise data for fuzzy querying. Fuzzy Sets and Systems, 140-1, 111–128.
Ullman, J. (1988).Principles of database and knowledge-base systems. Rockville, Maryland: Computer Science.
Umano, M. (1982). , Chapt. FREEDOM-0: a fuzzy database system. In M. Gupta, & E., Sanchez E. (Eds.), Fuzzy Information and Decision Processes (pp. 339–347). Amsterdam, The Netherlands: North-Holland
Zadeh, L. (1965). Fuzzy sets. Information and Control,8, 338–353
Zadeh, L. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems,1, 3–28
Zadrozny, S., & Kacprzyk, J. (1998). Implementing fuzzy querying via the internet/WWW: Java applets, activeX controls and cookies. In Proceedings of the workshop FQAS’98 (Flexible query answering systems) Roskilde, Denmark (pp. 358–369). Berlin Heidelberg New York: Springer.
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Haemmerlé, O., Buche, P. & Thomopoulos, R. The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data. J Intell Inf Syst 29, 279–304 (2007). https://doi.org/10.1007/s10844-006-0014-z
- Information integration
- Fuzzy sets
- Conceptual graphs