Adaptive Semantic Matching in a Multilingual Context

Abstract

In an increasingly multilingual digital world, information management tools must support the simultaneous use and matching of multiple natural languages. A prerequisite for this is that the underlying database engine seamlessly processes multilingual data across languages. However, most natural language processing-based techniques have focused on developing monolingual matching algorithms, often ignoring context knowledge and external domain-based sources, which lead to incomplete and inaccurate matching results in a multilingual environment. The purpose of this study is to propose an adaptive semantic matching method with context knowledge and user involvement as two new dimensions for matching the semantically related entities ontologies. We present a comprehensive evaluation of our solution by applying it in a multilingual e-commerce platform case study, which performed well on matching accuracy.