License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2013.35
URN: urn:nbn:de:0030-drops-42690
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2013/4269/
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Fran├ža, Manoel V. M. ; Garcez, Artur S. D. ; Zaverucha, Gerson

Relational Knowledge Extraction from Attribute-Value Learners

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Abstract

Bottom Clause Propositionalization (BCP) is a recent propositionalization method which allows fast relational learning. Propositional learners can use BCP to obtain accuracy results comparable with Inductive Logic Programming (ILP) learners. However, differently from ILP learners, what has been learned cannot normally be represented in first-order logic. In this paper, we propose an approach and introduce a novel algorithm for extraction of first-order rules from propositional rule learners, when dealing with data propositionalized with BCP. A theorem then shows that the extracted first-order rules are consistent with their propositional version. The algorithm was evaluated using the rule learner RIPPER, although it can be applied on any propositional rule learner. Initial results show that the accuracies of both RIPPER and the extracted first-order rules can be comparable to those obtained by Aleph (a traditional ILP system), but our approach is considerably faster (obtaining speed-ups of over an order of magnitude), generating a compact rule set with at least the same representation power as standard ILP learners.

BibTeX - Entry

@InProceedings{frana_et_al:OASIcs:2013:4269,
  author =	{Manoel V. M. Fran{\c{c}}a and Artur S. D. Garcez and Gerson Zaverucha},
  title =	{{Relational Knowledge Extraction from Attribute-Value Learners}},
  booktitle =	{2013 Imperial College Computing Student Workshop},
  pages =	{35--42},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-63-7},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{35},
  editor =	{Andrew V. Jones and Nicholas Ng},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2013/4269},
  URN =		{urn:nbn:de:0030-drops-42690},
  doi =		{10.4230/OASIcs.ICCSW.2013.35},
  annote =	{Keywords: Relational Learning, Propositionalization, Knowledge Extraction}
}

Keywords: Relational Learning, Propositionalization, Knowledge Extraction
Collection: 2013 Imperial College Computing Student Workshop
Issue Date: 2013
Date of publication: 14.10.2013


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