Metadata Enrichment via Topic Models for Author Name Disambiguation
This paper tackles the well known problem of Author Name Disambiguation (AND) in Digital Libraries (DL). Following [14,13], we assume that an individual tends to create a distinctively coherent body of work that can hence form a single cluster containing all of his/her articles yet distinguishing them from those of everyone else with the same name. Still, we believe the information contained in a DL may be not sufficient to allow an automatic detection of such clusters; this lack of information becomes even more evident in federated digital libraries, where the labels assigned by librarians may belong to different controlled vocabularies or different classification systems, and in digital libraries on the web where records may be not assigned neither subject headings nor classification numbers. Hence, we exploit Topic Models, extracted from Wikipedia, to enhance records metadata and use Agglomerative Clustering to disambiguate ambiguous author names by clustering together similar records; records in different clusters are supposed to have been written by different people. We investigate the following two research questions: (a) are the Classification Systems and Subject Heading labels manually assigned by librarians general and informative enough to disambiguate Author Names via clustering techniques? (b) Do Topic Models induce from large corpora the conceptual information necessary for labelling automatically DL metadata and grasp topic similarities of the records? To answer these questions, we will use the Library Catalogue of the Bolzano University Library as case study.
KeywordsDigital Libraries Topic Models Author Name Disambiguation
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- 2.Di Lauro, T., Choudhury, G.S., Patton, M., Warner, J.W., Brown, E.W.: Automated name authority contol and enhanced searching in the levy collection. D-Lib Magazine 7(4) (2001)Google Scholar
- 3.Han, H., Zha, H., Lee Giles, C.: Name disambiguation in author citations using a k-way spectral clustering method. In: JCDL 2005: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 334–343. ACM, New York (2005)Google Scholar
- 4.Heinrich, G.: Parameter estimation for text analysis, Technical report, University of Leipzig (2008)Google Scholar
- 7.Le, D.-T., Nguyen, C.-T., Ha, Q.-T., Phan, X.H., Horiguchi, S.: Matching and ranking with hidden topics towards online contextual advertising. In: Web Intelligence, Sydney, NSW, Australia, pp. 888–891 (2008)Google Scholar
- 9.On, B.-W., Lee, D., Kang, J., Mitra, P.: Comparative study of name disambiguation problem using a scalable blocking-based framework. In: JCDL 2005: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 344–353. ACM, New York (2005)Google Scholar
- 11.Phan, X.-H., Nguyen, C.-T., Le, D.-T., Nguyen, L.-M., Horiguchi, S., Ha, Q.-T.: A hidden topic-based framework towards building applications with short web documents. IEEE Transactions on Knowledge and Data Engineering 99 (2010) (prePrints)Google Scholar
- 12.Steyvers, M., Griffiths, T.: Probablistic topic models. In: Landauer, T., McNamara, D., Dennis, S., Kintsch, W. (eds.) Latent Semantic Anaylsis: A Road to Meaning. Lawrence Erlbaum, Mahwah (2006)Google Scholar