Abstract
Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build representations of these documents which keep their semantics as much as possible and are also suitable for efficient similarity calculation. As we describe in Koopman et al. (Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey, 29 June to 3 July, 2015. Bogaziçi University Printhouse. http://www.issi2015.org/files/downloads/all-papers/1042.pdf, 2015), the metadata of articles in the Astro dataset contribute to a semantic matrix, which uses a vector space to capture the semantics of entities derived from these articles and consequently supports the contextual exploration of these entities in LittleAriadne. However, this semantic matrix does not allow to calculate similarities between articles directly. In this paper, we will describe in detail how we build a semantic representation for an article from the entities that are associated with it. Base on such semantic representations of articles, we apply two standard clustering methods, K-Means and the Louvain community detection algorithm, which leads to our two clustering solutions labelled as OCLC-31 (standing for K-Means) and OCLC-Louvain (standing for Louvain). In this paper, we will give the implementation details and a basic comparison with other clustering solutions that are reported in this special issue.
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Notes
Since the sum of squares is the squared Euclidean distance, this is intuitively the “nearest” mean.
The CWTS-C5 and UMSI0 are the clustering solutions generated by two different methods, Infomap and the Smart Local Moving Algorithm (SLMA) respectively, applied on the direct citation network of articles. The two ECOOM clustering solutions are generated by applying the Louvain method to find communities among bibliographic coupled articles where ECOOM-NLP11 also incorporates the keywords information. The STS-RG clusters are generated by first projecting the small Astro dataset to the full Scopus database and collecting their cluster assignments after the full Scopus articles are clustered using SLMA on the direct citation network. More detailed account can be found in Velden et al. (2017).
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Acknowledgements
Part of this work has been funded by the COST Action TD1210 Knowescape. We would like to thank Jochen Gläser and Andrea Scharnhorst for extended comments on earlier versions of the text. We would also like to thank the internal reviewer Michael Heinz as well as the anonymous external referees for their valuable comments and suggestions.
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Wang, S., Koopman, R. Clustering articles based on semantic similarity. Scientometrics 111, 1017–1031 (2017). https://doi.org/10.1007/s11192-017-2298-x
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DOI: https://doi.org/10.1007/s11192-017-2298-x