A novel sentence similarity measure for semantic-based expert systems

A novel sentence similarity measure for semantic based expert systems is presented. The well-known problem in the fields of semantic processing, such as QA systems, is to evaluate the semantic similarity between irregular sentences. This paper takes advantage of corpus-based ontology to overcome this problem. A transformed vector space model is introduced in this article. The proposed two-phase algorithm evaluates the semantic similarity for two or more sentences via a semantic vector space. The first phase built part-of-speech (POS) based subspaces by the raw data, and the latter carried out a cosine evaluation and adopted the WordNet ontology to construct the semantic vectors. Unlike other related researches that focused only on short sentences, our algorithm is applicable to short (4–5 words), medium (8–12 words), and even long sentences (over 12 words). The experiment demonstrates that the proposed algorithm has outstanding performance in handling long sentences with complex syntax. The significance of this research lies in the semantic similarity extraction of sentences, with arbitrary structures.