Clustering articles based on semantic similarity

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. 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.

Special Issue of Scientometrics: Same data - different results? Towards a comparative approach to the identification of thematic structures in science

References in corpus (5)

• Fast unfolding of communities in large networks
• Modularity and community structure in networks
• Contextualization of topics: Browsing through the universe of bibliographic information
• Ariadne's Thread - Interactive Navigation in a World of Networked Information
• Contextualization of topics - browsing through terms, authors, journals and cluster allocations

Cited by in corpus (2)

• Mutual Information based labelling and comparing clusters
• Contextualization of topics: Browsing through the universe of bibliographic information