Name disambiguation in author citations using a K-way spectral clustering method

By Hui Han, Hongyuan Zha, C. Lee Giles. ACM/IEEE Joint Conference on Digital Libraries, 2005

315 citations recorded by the source corpus.

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3f347814b736d31dd9d51c81ec7eeab3214187e2

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In Artificial Intelligence

In Management Science and Operations Research

In Molecular Biology

Citations in the corpus

Author Name Disambiguation in MEDLINE (2009, methodology): “2003; MathSciNet1]; (b) community-based efforts [WikiAuthors2]; (c) unsupervised clustering that groups articles by similarity [Han et al. 2005; Soler 2007; Yin et al. 2007]; (d) supervised methods that utilize manually compiled training sets [Han et al.”
Making sense of credibility on the Web: Models for evaluating online information and recommendations for future research (2007, background): “... names coincide and overlap in the citation indexing systems calls for methods that distinguish the contexts in which authors or individuals appear, such as cross-document co-reference resolution algorithms that use the vector space model to resolve ambiguities (Bagga & Baldwin, 1998; Gooi & Alla”
Measuring semantic similarity between words using web search engines (2007, background): “Disambiguating named entities is important in various applications such as information retrieval [9], social network extraction [19, 3, 4], Word Sense Disambiguation (WSD) [21], citation matching [11] and cross-document co-reference resolution [28, 10].”