Semantic-driven bibliometric techniques for co-citation analysis

Abstract

Co-citation analysis can be exploited as a bibliometric technique used for mining information on the relationships between scientific papers. Proposed methods rely, however, on co-citation counting techniques that slightly take the semantic aspect into consideration. The present study proposes a semantic driven bibliometric techniques for co-citation analysis through measuring the semantic similarity (SS) between the titles of co-cited papers. Several computational measures rely on knowledge resources to quantify the semantic similarity, such as the WordNet “is a” taxonomy. Our proposal analyzes the SS between the titles of co-cited papers using word-based SS measures. Two major analytical experiments are performed: the first includes the benchmarks designed for testing word-based SS measures through the correlation coefficients for expressing the measures efficiency; the second exploits the dataset DBLP ${}^1$ citation network. As a result, the semantic similarity measures shows good performance in relation with the human judgements compared to automatic provided estimated similarities. Therefore, the lexical similarity can be consequently used for the automatic assessment of similarity between co-cited papers. The analysis of highly repeated co-citations demonstrates that the different SS measures display almost similar behaviours, with slight differences due to the distribution of the provided SS values. Furthermore, we note a low percentage of similar referred papers into the co-citations.