Author Name Disambiguation for Ranking and Clustering PubMed Data Using NetClus

摘要

The ranking and clustering of publication databases are of-ten used to discover useful information about research areas. NetClus is an iterative algorithm for clustering heterogenous star-schema infor-mation network that incorporates the ranking information of individual data types. The algorithm has been evaluated using the DBLP database. In this paper, we apply NetClus on PubMed, a free database of articles on life sciences and biomedical topics to discover key aspects of cancer research. The absence of unique identifiers for authors in PubMed intro-duces additional challenges. To address this, we introduce an improved author disambiguation technique using affiliation string normalisation based on vector space model together with co-author networks. Our tech-nique for disambiguating authors, which offers a higher accuracy than existing techniques, significantly improves NetClus clustering results.