Induction of logic programs: FOIL and related systems

Quinlan, J. R.; Cameron-Jones, R. Mike

doi:10.1007/bf03037228

Cited by 128 publications

(77 citation statements)

References 12 publications

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“…The size of incidents varied between 27 and 2646 nodes with an average size of 812 nodes. In addition to the approaches described above, we used the inductive logic programming algorithm Foil (Quinlan & Cameron-Jones, 1995) which is a well established approach to learning from relational data. Since Foil needs positive and negative examples for its rule induction, for each cluster we used one incident from all other clusters as negative example.…”

Section: T N I })mentioning

confidence: 99%

Incident Mining Using Structural Prototypes

Schmid

Hofmann

Bader

et al. 2010

Trends in Applied Intelligent Systems

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Abstract. Software and other technical products offered to a mass market have a high demand on support and help desks. A tool for automated classification of incident reports, errors and other customer requests which offers previous (successful) hints or solution procedures could efficiently decrease support costs. We propose an approach to mining incidents and other customer requests for support based on generalising structural prototypes from structured data. Retrieval can then be efficiently realised by matching incoming requests against prototypes. We present an application to incident reports in an SAP business information system. Several variants of structure generalisation algorithms were realised and performance for an example test base was evaluated with promising results.

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Section: T N I })mentioning

confidence: 99%

Incident Mining Using Structural Prototypes

Schmid

Hofmann

Bader

et al. 2010

Trends in Applied Intelligent Systems

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“…-Being based on logic programming, it can build on the successes of inductive logic programming [Muggleton and De Raedt, 1994;Quinlan and Cameron-Jones, 1995;Muggleton, 1995]. The fact that parts of ICL theories are logic programs should aid in this effort.…”

Section: Icl and Learningmentioning

confidence: 99%

The Independent Choice Logic and Beyond

Poole

2008

Lecture Notes in Computer Science

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Abstract. The Independent Choice Logic began in the early 90's as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.

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“…Then we generated extraction rules for each pair of attributes by using the FOIL program ( [15]), as described in [2]. The following 5 rules were generated: (NA = name, AD = address, DE = description, PE = period, RO = roomtype, and PR = price):…”

Section: Fig 1 An Xhtml Document Fragment and Its Graphic Viewmentioning

confidence: 99%

“…In our opinion, the advantage of our proposal is the use of the right tool for tackling a given task, i.e. for learning extraction rules it employs inductive logic programming (ILP) systems ( [15]), and for performing the extraction it employs XSLT technology ( [4]). …”

Section: Introductionmentioning

confidence: 99%

Mining Travel Resources on the Web Using L-Wrappers

Popescu

Bădică

Bǎdicǎ

2006

Artificial Intelligence and Soft Computing – ICAISC 2006

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Abstract. The work described here is part of an ongoing research on the application of general-purpose inductive logic programming, logic representation of wrappers (L-wrappers) and XML technologies (including the XSLT transformation language) to information extraction from the Web. The L-wrappers methodology is based on a sound theoretical approach and has already proved its efficacy on a smaller scale, in the area of collecting product information. This paper proposes the use of L-wrappers for tuple extraction from HTML in the domain of e-tourism. It also describes a method for translating L-wrappers into XSLT and illustrates it with the example of a real-world travel agency Web site.

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Induction of logic programs: FOIL and related systems

Cited by 128 publications

References 12 publications

Incident Mining Using Structural Prototypes

Incident Mining Using Structural Prototypes

The Independent Choice Logic and Beyond

Mining Travel Resources on the Web Using L-Wrappers

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