A Comparison of Graph Centrality Algorithms For Semantic Distance

Turan E., Arslan E., Tülü Ç., Orhan U.

Lapseki Meslek Yüksekokulu Uygulamalı Araştırmalar Dergisi, vol.1, no.2, pp.61-70, 2020 (Peer-Reviewed Journal)


Semantic networks are kind of datasets used for natural language processing. Distance measurement for semantic networks, which are generally based on graph structure, is a vital requirement for semantic analysis on concepts. Centrality measures can be used for calculating semantic distance between concepts in a semantic network. In this paper, we evaluated graph centrality algorithms including PageRank, HITS and Betweenness Centrality on a semantic network which was created from a Turkish dictionary. Centrality measures special to these algorithms are used to calculate semantic distance between synonym pairs in the semantic network. And we used a simple centrality method beside other three popular centrality algorithms to find out the most accurate and cost-effective method on our semantic network. Working on a bipartite model of the network which increases the complexity of implementation for centrality algorithms and performing calculations on a semantic network that can be expanded with new nodes and edges in periods of time are two major challenges to overcome. Considering all these conditions, results from each algorithm are compared to pick out an optimal method for the semantic network we created.