R. Mooney, P. Melville, L. Tang, J. Shavlik, I. Dutra, D. Page & V. Santos Costa (2002).
Relational Data Mining with Inductive Logic Programming for Link Discovery. Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, Maryland, USA.
A longer and updated version of this paper appears as a chapter in ``Data Mining: Next Generation Challenges and Future Directions'', H. Kargupta and A. Joshi (eds.), by AAAI/MIT Press

This publication is available in PDF and available in postscript.

Abstract:

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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