Inductive Logic Programming (ILP) Algorithm: Difference between revisions
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* (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Inductive_logic_programming Retrieved:2014-5-6. | * (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Inductive_logic_programming Retrieved:2014-5-6. | ||
** '''Inductive logic programming</B> ('''ILP</B>) 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 example]]s. <P> | ** '''Inductive logic programming</B> ('''ILP</B>) 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 example]]s. <P> Schema: ''positive examples'' + ''negative examples'' + ''background knowledge'' => ''hypothesis''. <P> Inductive logic programming is particularly useful in [[bioinformatics]] and [[natural language processing]]. <P> The term ''Inductive Logic Programming</i> was first introduced <ref> [[Luc De Raedt]]. A Perspective on Inductive Logic Programming. The Workshop on Current and Future Trends in Logic Programming, Shakertown, to appear in Springer LNCS, 1999. [[CiteSeerX]]: </ref> in a paper by [[Stephen Muggleton]] in 1991. The term “<i>inductive</i>” here refers to [[Inductive reasoning|philosophical]] (i.e. suggesting a theory to explain observed facts) rather than [[mathematical induction|mathematical]] (i.e. proving a property for all members of a well-ordered set) induction. | ||
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Latest revision as of 00:29, 19 August 2021
An Inductive Logic Programming (ILP) Algorithm is an model-based eager supervised classification algorithm that can solve a Recursive Rule Learning Task.
- AKA: ILP, Inductive Logic Programming.
- Context:
- It can (typically) be used to solve a Knowledge-based Supervised Binary Classification Task.
- It can (typically) find a Hypothesis [math]\displaystyle{ H }[/math], such that
- [math]\displaystyle{ B \cup H \vDash e, \forall e \in E^+ }[/math]
- [math]\displaystyle{ B \cup H \nvDash e, \forall f \in E^- }[/math]
- [math]\displaystyle{ B \cup H }[/math] is consistent.
- assuming, [math]\displaystyle{ B \nvDash e, \exists e \in E^+ }[/math]
- It can be applied by a Inductive Logic Programming System.
- It can be a Rule Induction Algorithm.
- …
- Example(s):
- Counter-Example(s):
- See: Supervised Learning; First-Order Logic; Inductive Reasoning; Propositional Learner; Multi-Relational Data Mining, Analytical Learning Algorithm, Logic Programming, Mathematical Induction.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Inductive_logic_programming Retrieved:2014-5-6.
- 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.
The term Inductive Logic Programming was first introduced [1] in a paper by Stephen Muggleton in 1991. 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.
- 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.
- ↑ Luc De Raedt. A Perspective on Inductive Logic Programming. The Workshop on Current and Future Trends in Logic Programming, Shakertown, to appear in Springer LNCS, 1999. CiteSeerX:
2011
- (De Raedt, 2011a) ⇒ Luc De Raedt. (2011). “Inductive Logic Programming.” In: (Sammut & Webb, 2011) p.529
1991
- (Muggleton, 1991) ⇒ Stephen Muggleton. (1991). “Inductive Logic Programming.” In: Journal of New Generation Computing, 8(4). doi:10.1007/BF03037089