skip to main content
10.1145/1321440.1321600acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
poster

A constraint-based probabilistic framework for name disambiguation

Published: 06 November 2007 Publication History

Abstract

This paper is concerned with the problem of name disambiguation. By name disambiguation, we mean distinguishing persons with the same name. It is a critical problem in many knowledge management applications. Despite much research work has been conducted, the problem is still not resolved and becomes even more serious, in particular with the popularity of Web 2.0. Previously, name disambiguation was often undertaken in either a supervised or unsupervised fashion. This paper first gives a constraint-based probabilistic model for semi-supervised name disambiguation. Specifically, we focus on investigating the problem in an academic researcher social network (http://arnetminer.org). The framework combines constraints and Euclidean distance learning, and allows the user to refine the disambiguation results. Experimental results on the researcher social network show that the proposed framework significantly outperforms the baseline method using unsupervised hierarchical clustering algorithm.

References

[1]
Aswani, N., Bontcheva, K., and Cunningham, H. Mining information for instance unification. In Proceedings of ISWC '2006, pp. 329--342, 2006.
[2]
Basu, S., Bilenko, M., and Mooney, R. J. A probabilistic framework for semi-supervised clustering. In Proceedings of SIGKDD '2004, pp. 59--68, Seattle, USA, August 2004.
[3]
Bekkerman, R. and McCallum, A. Disambiguating web appearances of people in a social network, In Proceedings of WWW '2005, pp. 463--470, ACM Press, 2005.
[4]
Bhattacharya, I. and Getoor, L. Entity resolution in graphs. Book Chapter in Mining Graph Data, Lawrence B. Holder and Diane J. Cook, Editors, Wiley, 2006.
[5]
Cohn, D., Caruana, R., and McCallum, A. Semi-supervised clustering with user feedback. Technical Report TR2003-1892, Cornell University, 2003.
[6]
Han, H., Giles, L., Zha, H., Li, C., and Tsioutsiouliklis, K. Two supervised learning approaches for name disambiguation in author citations. In Proceedings of JCDL '2004, Arizona, USA, pp. 296--305, 2004.
[7]
Han, H., Zha, H., and Giles, C. L. Name disambiguation in author citations using a K-way Spectral Clustering Method. In Proceedings of JCDL '2005, Denver, Colorado, USA, June 2005, 334--343.
[8]
Minkov, E., Cohen, W. W., and Ng, A. Y. Contextual search and name disambiguation in email using graphs. In Proceedings of SIGIR '2006, Washington, USA, pp. 27--34, 2006.
[9]
Tan, Y. F., Kan, M., and Lee, D. Search engine driven author disambiguation. In Proceedings of JCDL '2006, NC, USA, pp. 314--315, June 2006.
[10]
Tang, J., Hong, M., Zhang, J., Liang, B., Yao, L., and Li, J. ArnetMiner: toward building and mining social networks. (Demo). In Proceedings of SIGKDD '2007.

Cited By

View all
  • (2023)Author Name Disambiguation based on Capsule Network via Semantic and Structural FeaturesProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614053(293-300)Online publication date: 14-Jul-2023
  • (2023)A Heuristic Approach to Solve Author Name Ambiguity Using Minimum Bibliographic EvidencesSN Computer Science10.1007/s42979-023-02176-34:6Online publication date: 26-Sep-2023
  • (2020)Learning semantic and relationship joint embedding for author name disambiguationNeural Computing and Applications10.1007/s00521-020-05088-yOnline publication date: 20-Jun-2020
  • Show More Cited By

Index Terms

  1. A constraint-based probabilistic framework for name disambiguation

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
        November 2007
        1048 pages
        ISBN:9781595938039
        DOI:10.1145/1321440
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 06 November 2007

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. digital library
        2. name disambiguation
        3. semi-supervised clustering
        4. social network analysis

        Qualifiers

        • Poster

        Conference

        CIKM07

        Acceptance Rates

        Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

        Upcoming Conference

        CIKM '25

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)2
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 14 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2023)Author Name Disambiguation based on Capsule Network via Semantic and Structural FeaturesProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614053(293-300)Online publication date: 14-Jul-2023
        • (2023)A Heuristic Approach to Solve Author Name Ambiguity Using Minimum Bibliographic EvidencesSN Computer Science10.1007/s42979-023-02176-34:6Online publication date: 26-Sep-2023
        • (2020)Learning semantic and relationship joint embedding for author name disambiguationNeural Computing and Applications10.1007/s00521-020-05088-yOnline publication date: 20-Jun-2020
        • (2019)Name disambiguation using meta clusters and clustering ensembleJournal of Intelligent & Fuzzy Systems10.3233/JIFS-179519(1-10)Online publication date: 5-Nov-2019
        • (2019)Author Name Disambiguation Using Graph Node Embedding Method2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2019.8791898(410-415)Online publication date: May-2019
        • (2019)Diting: An Author Disambiguation Method Based on Network Representation LearningIEEE Access10.1109/ACCESS.2019.29424777(135539-135555)Online publication date: 2019
        • (2018)Researcher Name Disambiguation: Feature Learning and Affinity Propagation ClusteringFoundations of Intelligent Systems10.1007/978-3-030-01851-1_22(225-235)Online publication date: 7-Oct-2018
        • (2017)Name Disambiguation in Anonymized Graphs using Network EmbeddingProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132873(1239-1248)Online publication date: 6-Nov-2017
        • (2017)Name Disambiguation for Chinese Scientific Authors with Multi-Level Clustering22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)10.1109/CSE-EUC.2017.39(176-182)Online publication date: Jul-2017
        • (2017)Use of ResearchGate and Google CSE for author name disambiguationScientometrics10.1007/s11192-017-2341-y111:3(1965-1985)Online publication date: 1-Jun-2017
        • Show More Cited By

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media