A Fuzzy OWL Ontologies Embedding for Complex Ontology Alignments.

The semantic heterogeneity concern in the information integration can be handled by applying ontology alignment. The purpose of the ontology alignment procedure is to locate concepts that are semantically identical in two ontologies. But, one of these alignments' downsides is the lack of expressiveness and uncertainties, which can be accounted by using fuzzy complex alignments. To address this issue, the use of an effective strategy, consisting of two parts, is applied. We proceeded by establishing of a fuzzification approach that enables a semantic representation of both crisp and fuzzy data. The next step was to model fuzzy OWL 2 ontologies in vector space by a semantic embedding-based ontology matching technique and compute their similarity scores to determine the correlation levels. Then, it is reinforced by a stable marriage-based alignment extraction algorithm to establish a high-quality matching. Our proposed alignment scheme has been validated and reviewed on the benchmark tracks supplied by the Ontology Alignment Evaluation Initiative (OAEI). Experimental findings demonstrated the effectiveness of our matching method.