Construction of a large-scale test set for author disambiguation

Kang, In-Su; Kim, Pyung; Lee, Seungwoo; Jung, Hanmin; You, Beom-Jong

doi:10.1016/j.ipm.2010.10.001

Cited by 43 publications

(39 citation statements)

References 12 publications

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“…Some investigators have offered methods to assess the cost of name disambiguation in natural language processing [31] and bibliographic analysis [16]. Others have taken steps to establish and improve benchmark datasets [32]–[35] and to encourage participation in improving matching algorithms through competition at an institutional level [36].…”

Section: Introductionmentioning

confidence: 99%

Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?

Fegley

Torvik

2013

PLoS ONE

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The assumption that a name uniquely identifies an entity introduces two types of errors: splitting treats one entity as two or more (because of name variants); lumping treats multiple entities as if they were one (because of shared names). Here we investigate the extent to which splitting and lumping affect commonly-used measures of large-scale named-entity networks within two disambiguated bibliographic datasets: one for co-author names in biomedicine (PubMed, 2003–2007); the other for co-inventor names in U.S. patents (USPTO, 2003–2007). In both cases, we find that splitting has relatively little effect, whereas lumping has a dramatic effect on network measures. For example, in the biomedical co-authorship network, lumping (based on last name and both initials) drives several measures down: the global clustering coefficient by a factor of 4 (from 0.265 to 0.066); degree assortativity by a factor of ∼13 (from 0.763 to 0.06); and average shortest path by a factor of 1.3 (from 5.9 to 4.5). These results can be explained in part by the fact that lumping artificially creates many intransitive relationships and high-degree vertices. This effect of lumping is much less dramatic but persists with measures that give less weight to high-degree vertices, such as the mean local clustering coefficient and log-based degree assortativity. Furthermore, the log-log distribution of collaborator counts follows a much straighter line (power law) with splitting and lumping errors than without, particularly at the low and the high counts. This suggests that part of the power law often observed for collaborator counts in science and technology reflects an artifact: name ambiguity.

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Section: Introductionmentioning

confidence: 99%

Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?

Fegley

Torvik

2013

PLoS ONE

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“…The first real collection, derived from DBLP, 5 hereafter referred to as KISTI, was built at the Korea Institute of Science and Technology Information (Kang, Kim, Lee, Jung, & You, 2011) for English homonym author name disambiguation. This collection has 41,659 name instances of 867 name groups and 6,908 authors.…”

Section: Collectionsmentioning

confidence: 99%

Incremental author name disambiguation by exploiting domain‐specific heuristics

Santana

Gonçalves

Laender

et al. 2016

Asso for Info Science & Tech

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The vast majority of the current author name disambiguation solutions are designed to disambiguate a whole digital library (DL) at once considering the entire repository. However, these solutions besides being very expensive and having scalability problems, also may not benefit from eventual manual corrections, as they may be lost whenever the process of disambiguating the entire repository is required. In the real world, in which repositories are updated on a daily basis, incremental solutions that disambiguate only the newly introduced citation records, are likely to produce improved results in the long run. However, the problem of incremental author name disambiguation has been largely neglected in the literature. In this article we present a new author name disambiguation method, specially designed for the incremental scenario. In our experiments, our new method largely outperforms recent incremental proposals reported in the literature as well as the current state‐of‐the‐art non‐incremental method.

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“…The second collection derived from DBLP, hereafter referred to as KISTI, was built by the Korea Institute of Science and Technology Information [16] for English homonym author name disambiguation. The top 1000 most frequent author names from late-2007 DBLP citation records were obtained jointly with their citation records.…”

Section: Collectionsmentioning

confidence: 99%

Combining domain-specific heuristics for author name disambiguation

Santana¹,

Gonçalves²,

Laender³

et al. 2014

IEEE/ACM Joint Conference on Digital Libraries

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Author name disambiguation has been one of the hardest problems faced by digital libraries since their early days. Historically, supervised solutions have empirically outperformed those based on heuristics, but with the burden of having to rely on manually labelled training sets for the learning process. Moreover, most supervised solutions just apply some type of generic machine learning solution and do not exploit specific knowledge about the problem. In this paper, we follow a similar reasoning, but in the opposite direction. Instead of extending an existing supervised solution, we propose a set of carefully designed heuristics and similarity functions and apply supervision only to optimize such parameters for each particular dataset. As our experiments show, the result is a very effective, efficient and practical author name disambiguation method that can be used in many different scenarios.

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Construction of a large-scale test set for author disambiguation

Cited by 43 publications

References 12 publications

Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?

Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?

Incremental author name disambiguation by exploiting domain‐specific heuristics

Combining domain-specific heuristics for author name disambiguation

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