A Unified Probabilistic Framework for Name Disambiguation in Digital Library

Tang, Jie; Fong, A.C.M.; Wang, Bo; Zhang, Jing

doi:10.1109/tkde.2011.13

Cited by 215 publications

(164 citation statements)

References 39 publications

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“…They define six types of constraints and employ EM algorithm to learn the HRMF model parameters. In another work, Tang et al [23,26] present two name disambiguation methods that are based on pairwise factor graph model. They target name disambiguation in academic datasets.…”

Section: Related Workmentioning

confidence: 99%

“…From methodological point of view, some of the works follow a supervised learning approach [8,10], while others use unsupervised clustering [5,9,17,25]. There exist quite a few solutions that use graphical models [3,23,26,31]. What is common among all these works is that they use many biographical features including name, and affiliation, so they cannot protect the privacy of the actors in the dataset.…”

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confidence: 99%

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Name disambiguation from link data in a collaboration graph using temporal and topological features

Saha

Zhang

Hasan

2015

Soc. Netw. Anal. Min.

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In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Name disambiguation from link data in a collaboration graph using temporal and topological features

Saha

Zhang

Hasan

2015

Soc. Netw. Anal. Min.

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“…Recently, Tang et al ( [8,11]) presents two closely-related methods based on Pairwise Factor Graph models. The authorship is modeled as edges between observation variables (papers) and hidden variables (author labels).…”

Section: Related Workmentioning

confidence: 99%

“…As in [12,8], we use Pairwise Precision, Pairwise Recall, and Pairwise F1 scores to evaluate the performance of our method and other methods. Specifically, any two papers that are annotated with the same label in the ground truth are called a correct pair, and any two papers that are predicted with the same label (if they are grouped in the same cluster, we also call they have the same label) by a system but are labeled differently in the ground truth are called a wrong pair.…”

Section: Experimental Settingmentioning

confidence: 99%

“…Given two sets of categorical data, previous methods of name disambiguation use set similarity measures, such as Jaccard Coefficient ( [2,12]) or cosine similarity ( [8]), which often fail when the sets are unbalanced in cardinality, or when the frequencies of the elements in each set have distinctive patterns (to be explained in Section 4). We exploit the property that categorical sets from the same author follow similar distributions, and propose a generative probabilistic model to estimate the similarity of two sets.…”

Section: Introductionmentioning

confidence: 99%

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Author Name Disambiguation Using a New Categorical Distribution Similarity

Cong

Miao

2012

Machine Learning and Knowledge Discovery in Databases

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Abstract. Author name ambiguity has been a long-standing problem which impairs the accuracy of publication retrieval and bibliometric methods. Most of the existing disambiguation methods are built on similarity measures, e.g., "Jaccard Coefficient", between two sets of papers to be disambiguated, each set represented by a set of categorical features, e.g., coauthors and published venues 1 . Such measures perform bad when the two sets are small, which is typical in Author Name Disambiguation. In this paper, we propose a novel categorical set similarity measure. We model an author's preference, e.g., to venues, using a categorical distribution, and derive a likelihood ratio to estimate the likelihood that the two sets are drawn from the same distribution. This likelihood ratio is used as the similarity measure to decide whether two sets belong to the same author. This measure is mathematically principled and verified to perform well even when the cardinalities of the two compared sets are small. Additionally, we propose a new method to estimate the number of distinct authors for a given name based on the name statistics extracted from a digital library. Experiment shows that our method significantly outperforms a baseline method, a widely used benchmark method, and a real system.

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Citation‐based bootstrapping for large‐scale author disambiguation

Levin

Krawczyk

Bethard

et al. 2012

Acta Anaesthesiol Scand

107

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We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first "bootstrap" stage, a collection of highprecision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our selfsupervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B 3 F1 of .807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from .748 to .767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1.

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A Unified Probabilistic Framework for Name Disambiguation in Digital Library

Cited by 215 publications

References 39 publications

Name disambiguation from link data in a collaboration graph using temporal and topological features

Name disambiguation from link data in a collaboration graph using temporal and topological features

Author Name Disambiguation Using a New Categorical Distribution Similarity

Citation‐based bootstrapping for large‐scale author disambiguation

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