An approach for measuring semantic similarity between Wikipedia concepts using multiple inheritances. Issue 3 (May 2020)

• Record Type: Journal Article
• Authors: Hussain, Muhammad Jawad; Wasti, Shahbaz Hassan; Huang, Guangjian; Wei, Lina; Jiang, Yuncheng; Tang, Yong
• Is Part Of: Information processing & management. Volume 57:Issue 3(2020:May)
• Publication Date: 2020-05
• Subjects: Semantic similarity -- Multiple inheritance -- Information content -- Wikipedia category graph -- Knowledge graph; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.102188
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We addresses the limitations in traditional MICA-based and multiple inheritance-based approaches regarding Wikipedia Category Graph (WCG) structure. We highlights some new structural issues in WCG in terms of size, branching factors, and multiple inheritances. This paper presents a new approach called Neighborhood Ancestor Semantic Contribution (NASC) to compute SS between two Wikipedia concepts. Abstract: Wikipedia provides a huge collaboratively made semi-structured taxonomy called Wikipedia category graph (WCG), which can be utilized as a Knowledge Graph (KG) to measure the semantic similarity (SS) between Wikipedia concepts. Previously, several Most Informative Common Ancestor-based (MICA-based) SS methods have been proposed by intrinsically manipulating the taxonomic structure of WCG. However, some basic structural issues in WCG such as huge size, branching factor and multiple inheritance relations hamper the applicability of traditional MICA-based and multiple inheritance-based approaches in it. Therefore, in this paper, we propose a solution to handle these structural issues and present a new multiple inheritance-based SS approach, called Neighborhood Ancestor Semantic Contribution (NASC). In this approach, firstly, we define the neighborhood of a category (a taxonomic concept in WCG) to define its semantic space. Secondly, we describe the semantic value of a category by aggregating the intrinsic IC-based semantic contribution weights of its semantically relevant multiple ancestors. Thirdly, based on our approach, we propose six different methods to compute the SS between Wikipedia concepts. Finally, we evaluate our methods on gold standard word similarity benchmarks for English, German, Spanish and French languages. The experimental evaluation demonstrates that the proposed NASC-based methods remarkably outperform traditional MICA-based and multiple inheritance-based approaches.