Automated Name Authority Control and Enhanced Searching in the Levy Collection

DiLauro, Tim; Choudhury, Sayeed; Patton, Mark; Warner, James W.; Brown, Elizabeth W.

doi:10.1045/april2001-dilauro

Cited by 13 publications

(16 citation statements)

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“…al. [40,15] propose a semi-automatic algorithm using Bayes probabilities to disambiguate composers and artists in the Levy music collection. However, their algorithm largely depends on the Library of Congress name authority file.…”

Section: Introductionmentioning

confidence: 99%

“…Much research has been done to address the identity uncertainty problem in different fields using different methods, such as record linkage [17], duplicate record detection and elimination [8,26,29], merge/purge [22], data association [6], database hardening [11], citation matching [28], name matching [7,37,9], and name authority work in library cataloging practice [40,15,19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two supervised learning approaches for name disambiguation in author citations

Han

Giles

Zha

et al. 2004

Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital Libraries

305

316

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Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper investigates two supervised learning approaches to disambiguate authors in the citations 1 . One approach uses the naive Bayes probability model, a generative model; the other uses Support Vector Machines(SVMs) [39] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: co-author names, the title of the paper , and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the web, mainly publication lists from homepages, the other collected from the DBLP citation databases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two supervised learning approaches for name disambiguation in author citations

Han

Giles

Zha

et al. 2004

Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital Libraries

305

316

View full text Add to dashboard Cite

show abstract

“…Much research has been done to address the identity uncertainty problem using different methods, such as record linkage [21], duplicate record detection and elimination [10,31,35], merge/purge [27], data association [8], database hardening [13], citation matching [34,34], name matching [9,45,11], name equivalence identification [20], address matching [15], and name authority control in library cataloging practice [48,17,24]. At the concept level these methods include word sense disambiguation [47,30].…”

Section: Prior Workmentioning

confidence: 99%

“…Name authority control aims to find the authoritative form of names, i.e., the unambiguous reference to an individual [17]. Getty's ULAN (Union List of Artist's Names) [2] and the Library of Congress name authority file [3] are good examples of such authorized names.…”

Section: Prior Workmentioning

confidence: 99%

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

Han

Zha

Giles

2005

Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries

208

212

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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) 1 . 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 2 . 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.

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“…Only when one person is well-known, can she/he be recognised as the same author for different names. For example, the works of Alice Hawthorne and Septimus Winner can be cross-referenced as a single author (DiLauro et al, 2001). However, the majority of ordinary article authors will not be so fortunate.…”

Section: Introductionmentioning

confidence: 99%

Personal name identification in the practice of digital repositories

Xia

2006

Program

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -To propose improvements to the identification of authors' names in digital repositories. Design/methodology/approach -Analysis of current name authorities in digital resources, particularly in digital repositories, and analysis of some features of existing repository applications. Findings -This paper finds that the variations of authors' names have negatively affected the retrieval capability of digital repositories. Two possible solutions include using composite identifiers that combine author name, publication date, and author affiliation, and also asking authors to input the variants of their name, if any, at the time of depositing articles. Originality/value -This is the first time that the approach of authors self-depositing their name variations is proposed. This approach will be able to reduce confusions in name identification.

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Automated Name Authority Control and Enhanced Searching in the Levy Collection

Cited by 13 publications

References 0 publications

Two supervised learning approaches for name disambiguation in author citations

Two supervised learning approaches for name disambiguation in author citations

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

Personal name identification in the practice of digital repositories

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