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

• Published in: Journal of informetrics Vol. 3; no. 1; pp. 49 - 63
• Main Authors: Ahlgren, Per, Colliander, Cristian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2009
• Subjects: Bibliometrics; Biblioteks- och informationsvetenskap; Citation data; Cluster analysis; Data source combination; Library and information science; Other social sciences; SAMHÄLLSVETENSKAP; Science mapping; SOCIAL SCIENCES; Text mining; Textual data; Övrig samhällsvetenskap; Textual data; Cluster analysis; Data source combination; Science mapping; Citation data
• ISSN: 1751-1577; 1875-5879; 1875-5879
• DOI: 10.1016/j.joi.2008.11.003

This paper treats document–document similarity approaches in the context of science mapping. Five approaches, involving nine methods, are compared experimentally. We compare text-based approaches, the citation-based bibliographic coupling approach, and approaches that combine text-based approaches and bibliographic coupling. Forty-three articles, published in the journal Information Retrieval, are used as test documents. We investigate how well the approaches agree with a ground truth subject classification of the test documents, when the complete linkage method is used, and under two types of similarities, first-order and second-order. The results show that it is possible to achieve a very good approximation of the classification by means of automatic grouping of articles. One text-only method and one combination method, under second-order similarities in both cases, give rise to cluster solutions that to a large extent agree with the classification.