The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Entity Resolution with Markov Logic
Abstract
Entity resolution is the problem of determining which records in a database refer to the same entities, and is a crucial and expensive step in the data mining process. Interest in it has grown rapidly, and many approaches have been proposed. However, they tend to address only isolated aspects of the problem, and are often ad hoc. This paper proposes a well-founded, integrated solution to the entity resolution problem based on Markov logic. Markov logic combines first-order logic and probabilistic graphical models by attaching weights to first-order formulas, and viewing them as templates for features of Markov networks. We show how a number of previous approaches can be formulated and seamlessly combined in Markov logic, and how the resulting learning and inference problems can be solved efficiently. Experiments on two citation databases show the utility of this approach, and evaluate the contribution of the different components.
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Cites
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Hapeman, D. E. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,373
Richardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676
Self-citationFellegi, I. P., & Sunter, A. B. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10501049 · Cited by 2,475
Collins, M. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118694 · Cited by 1,862
Logical Foundations of Artificial Intelligence
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Efficient clustering of high-dimensional data sets with application to reference matching
McCallum, A., Nigam, K., & Ungar, L. H. (2000) · Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/347090.347123 · Cited by 1,096
(2016) · Biostatistics in Public Health Using STATA · doi:10.1201/b19547-8 · Cited by 1,037
Cited by
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654
Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2014) · Machine Learning · doi:10.1007/s10994-013-5363-6 · Cited by 675
BLOG: Probabilistic Models with Unknown Objects
Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D. L., & Kolobov, A. (2007) · Introduction to Statistical Relational Learning · doi:10.7551/mitpress/7432.003.0015 · Cited by 390
Metadata
- Authors
- Singla, P., & Domingos, P.
- Venue
- Sixth International Conference on Data Mining (ICDM'06)
- Publisher
- IEEE
- Published
- 2006-12
- Pages
- 572-582
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2171472464
- Cited by
- 359
- References
- 35
- FWCI
- 25.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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Data sources
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.
