Abstract
Weighted fuzzy logic programs increase the expressivity of fuzzy logic programs by allowing the association of a significance weight with each atom in the body of a fuzzy rule. In this paper, we propose a prototype system for the practical integration of weighted fuzzy logic programs with relational database systems in order to provide efficient query answering services. In the system, a dynamic weighted fuzzy logic program is a set of rules together with a set of database queries, fuzzification transformations and fact derivation rules, which allow the provided set of rules to be augmented with a set of fuzzy facts retrieved from the underlying databases. The weights of the rules may be estimated by a neural network-based machine learning process using some specially designated for this purpose training database data.
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Chortaras, A., Stamou, G., Stafylopatis, A. (2007). Integrated Query Answering with Weighted Fuzzy Rules. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_67
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DOI: https://doi.org/10.1007/978-3-540-75256-1_67
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