## 5 Conclusions and Future Work  
BC is a similarity measure applicable to scientific articles that cite references, because it estimates the similarity between two articles by measuring how the two articles cite a similar set of references. BC is thus an effective and fundamental measure for retrieval, analysis, and mapping of scientific literature. However, BC has a main weakness: two related articles may still cite _different_ references. The proposed new measure, BCCCC, tackles the weakness of category-based CC, which estimates how these different references are related to each other. Development of category-based CC is 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 performance of BCCCC is evaluated by experiments and validated in a case study. The results show that BCCCC is an improved version of BC, as it performs significantly better than state-of-the-art variants of BC. The contribution of category-based CC to BC is thus justified. Moreover, effects of different settings for BCCCC are investigated as well. The results show that setting a proper parameter for BCCCC is not a difficult task, and article-based CC may still be helpful, although it is less helpful than category-based CC. We also investigated the potential contribution of BCCCC to biomedical search engines. The results show that BCCCC performs significantly better than the article recommendation service provided by PubMed, which is a popular search engine routinely employed by biomedical professionals. BCCCC can thus provide a different kind of helpful information to further improve the search engine, especially in recommending articles that are highly related to each other (i.e., focusing and reporting conclusive results on the same specific topics). An application of BCCCC is the identification of highly related articles. As noted above, BCCCC can be used to improve PubMed in recommending highly related articles, which is a service required by biomedical professionals, who often routinely analyze highly related articles on specific research issues. Identification of highly related articles is also required by domain experts that strive to maintain online databases of the associations already published in biomedical literature (e.g., CTD, GHR, and OMIM noted in Section 1). Maintenance of these databases is quite costly, as the domain experts need to routinely collect and analyze highly related articles to curate the databases. The associations already in the databases can be treated as categories for BCCCC to employ category-based CC to prioritize new articles that report conclusive results on the same associations. With the support of BCCCC, curation of new associations can be done in a more timely and comprehensive manner. The bibliographic coupling information provided by BCCCC may be used to improve other search engines in different domains as well. Another application of BCCCC is the improvement of scientometric techniques that have been used in various domains. BCCCC can be used to improve these techniques in retrieval, clustering, and classification of scientific literature. Moreover, BCCCC is an enhanced version of BC, which is often integrated with different measures. It is thus reasonable to expect that these measures can be further improved by incorporating the idea of BCCCC. BCCCC improves BC by category-based CC (i.e., _CitationSim_rsf_, see Equation (4)), which is integrated with a similarity component working on the titles of the references (i.e., _TextSim_rsf_, ref. Equation (2)). It is thus interesting to investigate how category-based CC can work with other kinds of text-based similarity components so that identification of highly related articles can be further improved. For example, text-based similarity can be measured by considering the abstracts of the references, rather than only the titles of the references.