This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper, the performance of the new feature-based algorithm is compared against the performance of seven ontology-based algorithms adapted to Arabic. The results of the evaluation and comparison experiments show that the new proposed algorithm outperforms the adapted word similarity algorithms on the Arabic word benchmark dataset. The proposed algorithm will be included in the AWN-similarity which is free open-source software for Arabic.
