## 1 Introduction  
Given two scientific articles \(a_{1}\) and \(a_{2}\), bibliographic coupling (BC) is a measure to estimate the similarity between \(a_{1}\) and \(a_{2}\) by considering how \(a_{1}\) and \(a_{2}\) cite a similar set of references [1]. BC works based on an expectation that two articles with a similar set of references may focus on related (or even the same) research issues. The expectation is justified in practice, as BC has been an effective measure for many literature analysis and mapping tasks, such as classification [2] and clustering [3,4] of scientific articles, as well as clustering of scientific journals [5]. Moreover, BC can be applied to more scientific articles, because it works on the references cited by two articles \(a_{1}\) and \(a_{2}\), and the _titles_ of these references are more publicly available than other kinds of information about \(a_{1}\) and \(a_{2}\), including full texts of \(a_{1}\) and \(a_{2}\), as well as how \(a_{1}\) and \(a_{2}\) are cited by others (many articles are even not cited by any article). BC is thus an effective measure to retrieve similar legal judgments [6] and detect plagiarism [7]as well. It can also be integrated with different similarity measures, such as those that work on main contents (titles, abstracts, and/or full texts) of articles to cluster articles and map research fields [3; 8]. Therefore, further improvement of BC is of practical and technical significance. An improved version of BC can be a fundamental component for the scientometric applications noted above. Equation (1) defines BC similarity between two articles \(a_{1}\) and \(a_{2}\), where R\({}_{a1}\) and R\({}_{a2}\) are the sets of references cited by \(a_{1}\) and \(a_{2}\) respectively. Therefore, the main weaknesses of BC include: (1) two related articles may still cite different references; and conversely, (2) two unrelated articles may happen to cite the same references. Many techniques have been developed to tackle the weaknesses by collecting additional information from different sources, including titles of the references cited by \(a_{1}\) and \(a_{2}\)[9; 10] and full texts of \(a_{1}\) and \(a_{2}\)[11; 12]); however, full texts of many articles are not publicly available. \[BC(a1,a2)=\frac{|R_{a1}\cap R_{a2}|}{|R_{a1}\cup R_{a2}|} \tag{1}\]  
In this paper, we aim at improving BC by tackling the first weakness noted above (i.e., two _related_ articles may cite _different_ references) without relying on full texts of the articles. A different kind of information is considered: category-based cocitation (category-based CC), which measures how \(a_{1}\) and \(a_{2}\) cite those references that are cited by articles in the _same categories_ about specific topics. A category contains a set of articles that focus on the same research topic (e.g., association between specific entities or events). As articles in the same category are highly related to each other, the references cited by these articles may be related to each other as well, and hence if \(a_{1}\) and \(a_{2}\) cite these references, the similarity between \(a_{1}\) and \(a_{2}\) can be increased, even though these references have different titles. The enhanced version of BC is thus named BCCCC (Bibliographic Coupling with Category-based Cocitation). Experimental results will show that BCCCC performs significantly better than several state-of-the-art variants of BC that worked on references and their titles, which are more publicly available than full texts of articles. The intended application of BCCCC is the identification of _highly related_ articles. Two articles are highly related only if they _focus_ on the _same specific_ research issues. Researchers often routinely strive to analyze highly related articles on specific research issues that are in their own interest. A typical example of such applications is the identification of articles that report conclusive results on associations of specific biomedical entities.