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Fuzzy systems may be considered as knowledge-based systems that incorporates human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions. The intent of this study is to present a fuzzy knowledge integration framework using a Novel Evolutionary Strategy (NES), which can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach consists of two phases: fuzzy knowledge encoding and fuzzy knowledge integration Four application domains, the hepatitis diagnosis, the sugarcane breeding prediction, Iris plants classification,and Tic-tac-toe endgame were used to show the performance of the proposed knowledge approach.Results show that the fuzzy knowledge base derived using our approach performs better than Genetic Algorithm based approach.

Abstract. Automatic ontology alignment tools perform matching between the concepts of two ontologies and provide a similarity measure for each pair of aligned concepts. However, none of the existing tools are perfect and multiple alignment tools produce varying similarity measures for a certain alignment. Also, the similarity measures provided by an alignment may not be helpful enough for indicating the degree of reliability. While using a random alignment tool we noticed that some quality alignments are given medium or even low similarity measures, and that causes the user ignoring those alignments. In this study we have proposed an ensemble model of ontology alignment that aggregates multiple alignment tools with the help of Fuzzy C Means clustering and Type 2 Fuzzy Membership Functions. We have shown that our approach helps the user to choose the best alignment results which has not been obtained by any other alignment tools we experimented with.

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