Assessing semantic similarity between concepts: A weighted‐feature‐based approach. (3rd January 2020)

• Record Type: Journal Article
• Authors: Wasti, Shahbaz Hassan; Hussain, Muhammad Jawad; Huang, Guangjian; Akram, Aftab; Jiang, Yuncheng; Tang, Yong
• Is Part Of: Concurrency and computation. Volume 32:Number 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-03
• Subjects: information content -- semantic similarity -- TFIDF -- Wikipedia; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5594
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Traditional feature‐based semantic similarity (SS) approaches exploit the Wikipedia features in term of sets. They evaluate the similarity of concepts based on the commonalities among their feature sets. However, these feature‐based approaches treat all the features equally in similarity evaluation. Therefore, they ignore the underlying statistics of the features and consequently lose the essential semantic details about them. One solution is that each feature can be assigned a specific weight using its statistics. This weight will reflect the relative importance of a feature in similarity evaluation. Therefore, in this paper, based on two statistical models, ie, information content and T F I D F, we propose some hybrid semantic similarity measurement methods. Firstly, we propose some new methods called weighting functions to compute the weights of the features and feature sets in Wikipedia. Secondly, based on the weighting functions, we propose some new weighted feature‐based SS approaches for Wikipedia concepts. Thirdly, we evaluate the proposed methods on well‐known benchmarks for English, German, French, and Spanish languages. Finally, we compare the performance of our methods with the traditional feature‐based and some state‐of‐the‐art SS approaches. The experimental evaluation shows that our weighted methods perform better than the traditional feature‐based and some state‐of‐the‐art approaches in similarity evaluation.