Semantic Similarity between Queries in QA System using a Domain-specific Taxonomy

Semantic similarity has been extensively studied in the past decades and has become a rapidly growing field of research. Sentence or short text similarity measures play an important role in text-based applications, such as text mining, information retrieval and question answering systems. In this paper we consider the problem of semantic similarity between queries in a question answering system with the purpose of query recommendation. Our approach is based on an existing domain-specific taxonomy. We define innovative three-layered semantic similarity measures between queries using existing similarity measures between ontology concepts combined with various set-based distance measures. We then analyse and evaluate our approach against human intuition using a data set of 90 questions. Further on, we argue that these measures are taxonomy-dependent and are influenced by various factors: taxonomy structure, keyword mappings, keyword weights, query-keyword mappings and the chosen concept similarity measure.

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