Bibliographic coupling, common abstract stems and clustering: A comparison of two document-document similarity approaches in the context of science mapping

Abstract

This paper deals with two document-document similarity approaches in the context of science mapping: bibliographic coupling and a text approach based on the number of common abstract stems. We used 43 articles, published in the journal Information Retrieval, as test articles. An information retrieval expert performed a classification of these articles. We used the cosine measure for normalization, and the complete linkage method was used for clustering the articles. A number of articles pairs were ranked (1) according to descending normalized coupling strength, and (2) according to descending normalized frequency of common abstract stems. The degree of agreement between the two obtained rankings was low, as measured by Kendall's tau. The agreement between the two cluster solutions, one for each approach, was fairly low, according to the adjusted Rand index. However, there were examples of perfect agreement between the coupling solution and the stems solution. The classification generated by the expert contained larger groups compared to the coupling and stems solutions, and the agreement between the two solutions and the classification was not high. According to the adjusted Rand index, though, the stems solution was a better approximation of the classification than the coupling solution. With respect to cluster quality, the overall Silhouette value was slightly higher for the stems solution. Examples of homogeneous cluster structures, as well as negative Silhouette values, were found with regard to both solutions. The expert classification indicates that the field of information retrieval, as represented by one volume of articles published in Information Retrieval, is fairly heterogeneous regarding research themes, since the classification is associated with 15 themes. The complete linkage method, in combination with the upper tail rule, gave rise to a fairly good approximation of the classification with respect to the number of identified groups, especially in case of the stems approach.AHLGREN & JARNEVING: Bibliographic coupling, common abstract stems and clustering 274 Scientometrics 76 (2008)

Detecting the intellectual structure of library and information science based on formal concept analysis

“…A significant portion of bibliometric analysis has been focused on citation analysis, which traces through citations and reveals the intellectual structure from a macro perspective. The representatives are direct citation (Garfield 2004), journal co-citation analysis (Hu et al 2011;Mustafee et al 2014), document co-citation analysis (Li et al 2010;Gao et al 2012), document bibliographic coupling (Ahlgren and Jarneving 2008), author co-citation analysis (ACA) (White and Griffith 1981;Chen and Lien 2011), and author bibliographic coupling (Zhao and Strotmann 2008a;Ma 2012). Another important branch of bibliometric analysis is content analysis, which relies on natural language processing to analyze large corpora of literature text and depicts the intellectual structure from a micro perspective.…”

Document–document similarity approaches and science mapping: Experimental comparison of five approaches

“…In this work, where the research reported in an earlier article (Ahlgren & Jarneving, 2008) is developed, we compare experimentally five approaches to document-document similarity in the context of science mapping. We compare (1) text-based approaches, (2) the citation-based bibliographic coupling approach, and (3) approaches that combine text-based approaches and bibliographic coupling.…”

Consensus formation in science modeled by aggregated bibliographic coupling

“…The concept of bibliographic coupling was introduced by Kessler (1963) who demonstrated the existence of the phenomenon and argued for its usefulness as an indicator of subject relatedness. However, as noted by Glänzel and Czerwon (1996) and De Bellis (2009) the technique lived a relatively quiet life until the 1990s when bibliometricians began to employ it for identifying and mapping clusters of subject-related documents (e.g., Ahlgren & Jarneving, 2008;Glänzel & Czerwon, 1996;Jarneving, 2007). As noted above, we believe that the bibliographic coupling technique has another promising potential -namely as a measure of the level of consensus in science.…”