Disjointness axioms between top-level ontology concepts as a heuristic for word similarity evaluation

Word semantic similarity is essential to numerous natural language processing and computational linguistics applications, including word sense disambiguation, machine translation, and information retrieval. Evaluating word similarity better and faster remains a complex problem due to the limitations of semantic similarity algorithms. In this paper, we propose a novel approach for computing word semantic similarity that uses the disjoint axioms between top-level ontology concepts as a heuristic for evaluating the semantic similarity between domain entities that represent the senses of those words. Our method leverages the disjointness information in top-level ontologies to reduce the number of comparisons needed to evaluate the semantic similarity between words. We evaluate our approach on benchmark datasets for the word similarity task. The results show that our method outperforms the traditional algorithms for knowledge-based word similarity regarding correlation with human judgments and time performance. Our proposed method can potentially facilitate the development of more effective and efficient applications.