## Inductive Logic Programming (ILP), Link Discovery (LD), Evidence Extraction and Link Discovery (EELD)

**Inductive logic programming** (**ILP**) is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

Schema: *positive examples* + *negative examples* + *background knowledge* => *hypothesis*.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Ehud Shapiro laid the theoretical foundation for inductive logic programming^{[1]}^{[2]} and built its first implementation (Model Inference System) in 1981:^{[3]} a Prolog program that inductively inferred logic programs from positive and negative examples. The term *Inductive Logic Programming* was first introduced^{[4]} in a paper by Stephen Muggleton in 1991.^{[5]} The term “*inductive*” here refers to philosophical (i.e. suggesting a theory to explain observed facts) rather than mathematical (i.e. proving a property for all members of a well-ordered set) induction.

**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

**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

http://www.cs.utexas.edu/~ml/papers/ld-bkchapter-04.pdf