Link discovery (LD) is an important task in data mining for counter-terrorism and is the focus of DARPA's Evidence Extraction and Link Discovery (EELD) research program. Link discovery concerns the identification of complex relational patterns that indicate potentially threatening activities in large amounts of relational data. Most data-mining methods assume data is in the form of a feature-vector (a single relational table) and cannot handle multi-relational data. Inductive logic programming is a form of relational data mining that discovers rules in first-order logic from multi-relational data. This paper discusses the application of ILP to learning patterns for link discovery.

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In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.

@inproceedings{mmt:nsfw02, title={Relational Data Mining with Inductive Logic Programming for Link Discovery}, author={Raymond J. Mooney and Prem Melville and Lappoon R. Tang and Jude Shavlik and Inês de Castro Dutra and David Page and Vítor Santos Costa}, booktitle={Proceedings of the National Science Foundation Workshop on Next Generation Data Mining}, month={November}, address={Baltimore, MD}, url="http://www.cs.utexas.edu/users/ai-lab?mmt:nsfw02", year={2002} }