Paul Hsiung, Andrew Moore, Daniel Neill, Jeff Schneider
Active Learning, Applications, Link Analysis
The problem of detecting aliases - multiple text string identifiers corresponding to the same entity - is increasingly important in the domains of biology, intelligence, marketing, and geoinformatics. Aliases arise from entities who are trying to hide their identities, from a person with multiple names, or from words which are uninten-tionally or even intentionally misspelled. While purely orthographic methods (e.g. string similarity) can help solve unintentional spelling cases, many types of alias (including those adopted with malicious intent) can fool these methods. However, if an entity has a changed name in some context, several or all of the set of other entities with which it has relationships can re-main stable. Thus, the local social network can be exploited by using the relationships as semantic information. The proposed combined algorithm takes ad-vantage of both orthographic and semantic information to detect aliases. By applying the best combination of both types of information, the combined algorithm outperforms the ones built solely on one type of information or the other. Empirical results on three real world data sets support this claim.
This is an extended version of the paper appearing in the proceedings of the International Conference on Intelligence Analysis 2005
An implementation of the algorithms from this paper is available here.
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Datasets used in this paper are available here.
@inproceedings{hsiung_alias,
Howpublished = {International Conference on Intelligence Analysis},
Month = {May},
Year = {2005},
Booktitle = {Proceedings of the International Conference on Intelligence Analysis},
Author = {
Paul Hsiung, Andrew
Moore, Daniel Neill, Jeff
Schneider
},
Title = {Alias Detection in Link Data Sets}
}