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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Kargupta, H., Joshi, A., Sivakumar K., and Yesha, Y., editors, Data Mining: Next Generation Challenges and Future Directions:239--254, Menlo Park, CA, 2004. AAAI Press.

@article{ld-bkchapter-04, title={Relational Data Mining with Inductive Logic Programming for Link Discovery}, author={Raymond J. Mooney and P. Melville and L. R. Tang and J. Shavlik and I. Dutra and D. Page}, journal={Data Mining: Next Generation Challenges and Future Directions}, editor={Kargupta, H., Joshi, A., Sivakumar K., and Yesha, Y.}, address={Menlo Park, CA}, publisher={AAAI Press}, pages={239--254}, url="http://www.cs.utexas.edu/users/ai-lab/?ld-bkchapter-04", year={2004} }