The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
A Latent Dirichlet Model for Unsupervised Entity Resolution
Abstract
Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other. Our approach differs from other recently proposed entity resolution approaches in that it is a) generative, b) does not make pair-wise decisions and c) captures relations between entities through a hidden group variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility and practicality of our relational entity resolution approach for author resolution in two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which relational information is useful.
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(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,223
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
A Bayesian Analysis of Some Nonparametric Problems
Ferguson, T. S. (1973) · The Annals of Statistics · doi:10.1214/aos/1176342360 · Cited by 4,697
Fellegi, I. P., & Sunter, A. B. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10501049 · Cited by 2,475
Markov Chain Sampling Methods for Dirichlet Process Mixture Models
Neal, R. M. (2000) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2000.10474879 · Cited by 2,288
Probabilistic Latent Semantic Analysis
Hofmann, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6705 · Cited by 2,047
Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
Antoniak, C. E. (1974) · The Annals of Statistics · doi:10.1214/aos/1176342871 · Cited by 1,972
Expectation Propagation for approximate Bayesian inference
Minka, T. P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.2294 · Cited by 1,392
A comparison of string distance metrics for name-matching tasks
Cohen, W. W., Ravikumar, P., & Fienberg, S. E. (2003) · Cited by 1,371
Cited by
Collective entity resolution in relational data
Bhattacharya, I., & Getoor, L. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217304 · Cited by 593
Self-citationLatent dirichlet allocation for tag recommendation
Krestel, R., Fankhauser, P., & Nejdl, W. (2009) · Proceedings of the third ACM conference on Recommender systems · doi:10.1145/1639714.1639726 · Cited by 494
A brief survey of automatic methods for author name disambiguation
Ferreira, A. A., Gonçalves, M. A., & Laender, A. H. F. (2012) · ACM SIGMOD Record · doi:10.1145/2350036.2350040 · Cited by 293
Metadata
- Authors
- Bhattacharya, I., & Getoor, L.
- Venue
- Proceedings of the 2006 SIAM International Conference on Data Mining
- Published
- 2006-04-20
- Pages
- 47-58
- OpenAlex
- W1536860849
- Cited by
- 266
- References
- 34
- FWCI
- 24.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
