Relational Data Mining with Inductive Logic Programming for Link Discovery

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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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2002
Accession Number
ADA537896

Entities

People

  • David M. Page
  • Ines De Castro Dutro
  • Jude Shavlik
  • Lappoon R. Tang
  • Prem Melville
  • Raymond J. Mooney
  • Vitor S. Costa

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Biomedical
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computer Programming
  • Computer Science
  • Crime
  • Data Mining
  • Data Sets
  • Databases
  • Extraction
  • Information Science
  • Learning
  • Machine Learning
  • Materials
  • Relational Databases
  • Terrorism

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.
  • Strategic Security Studies

Technology Areas

  • AI & ML