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- Ahlgren, Per, et al.
(author)
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Community Detection Using Citation Relations and Textual Similarities in a Large Set of PubMed Publications
- 2019
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In: PROCEEDINGS OF THE 17th INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS. - Leuven : International Society for Scientometrics and Informetrics. - 9788833811185 ; , s. 1380-1392
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Conference paper (peer-reviewed)abstract
- In this contribution, the effects of enhancing direct citations, with respect to publication-publication relatedness measurement, by indirect citation relations (bibliographic coupling and co-citation) and text relations on clustering accuracy are analyzed. In total, we investigate six approaches. In one of these, direct citations are enhanced by both bibliographic coupling and co-citation, whereas text relations are used to enhance direct citations in another approach. In addition to an approach based on direct citations only, we include in the study, for comparison reasons, each approach that is involved in the enhancement of direct citations. For the evaluation of the approaches, we use a methodology proposed by earlier research. However, the used evaluation criterion is based on MeSH, arguable the most sophisticated item-level classification scheme available. The results show that the co-citation approach has the worst performance, and that the direct citations approach is outperformed by the other four investigated approaches. An approach in which direct citations are enhanced by the BM25 textual relatedness measure has the best performance, followed by the approach that combines direct citations with bibliographic coupling and co-citation. The latter performs slightly better than the bibliographic coupling approach, which in turn has a better performance than the BM25 approach.
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