Bibliographic coupling (BC) is a similarity measure for scientific articles. It works based on an expectation that two articles that cite a similar set of references may focus on related (or even the same) research issues. For analysis and mapping of scientific literature, BC is an essential measure, and it can also be integrated with different kinds of measures. Further improvement of BC is thus of both practical and technical significance. In this paper, we propose a novel measure that improves BC by tackling its main weakness: two related articles may still cite different references. Category-based cocitation (category-based CC) is proposed to estimate how these different references are related to each other, based on the assumption that two different references may be related if they are cited by articles in the same categories about specific topics. The proposed measure is thus named BCCCC (Bibliographic Coupling with Category-based Cocitation). Performance of BCCCC is evaluated by experimentation and case study. The results show that BCCCC performs significantly better than state-of-the-art variants of BC in identifying highly related articles, which report conclusive results on the same specific topics. An experiment also shows that BCCCC provides helpful information to further improve a biomedical search engine. BCCCC is thus an enhanced version of BC, which is a fundamental measure for retrieval and analysis of scientific literature.

Featured Application: The technique presented in the paper can be used to build an issue-based clustering system, which is essential for exploration and curation of research issues and their findings reported in scientific literature. Abstract:A scholarly article often discusses multiple research issues. The clustering of scholarly articles based on research issues can facilitate analyses of related articles on specific issues in scientific literature. It is a task of overlapping clustering, as an article may discuss multiple issues, and hence, be clustered into multiple clusters. Clustering is challenging, as it is difficult to identify the research issues with which to cluster the articles. In this paper, we propose the use of the titles of the references cited by the articles to tackle the challenge, based on the hypothesis that such information may indicate the research issues discussed in the article. A technique referred to as ICRT (Issue-based Clustering with Reference Titles) was thus developed. ICRT works as a post-processor for various clustering systems. In experiments on those articles that domain experts have selected to annotate research issues about specific entity associations, ICRT works with various clustering systems that employ state-of-the-art similarity measures for scholarly articles. ICRT successfully improves these systems by identifying clusters of articles with the same research focuses on specific entity associations. The contribution is of technical and practical significance to the exploration of research issues reported in scientific literature (supporting the curation of entity associations found in the literature).