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Inductive Learning in Deductive Databases.

Saso Dzeroski, Nada Lavrac: Inductive Learning in Deductive Databases. IEEE Trans. Knowl. Data Eng. 5(6): 939-949(1993)
@article{DBLP:journals/tkde/DzeroskiL93,
  author    = {Saso Dzeroski and
               Nada Lavrac},
  title     = {Inductive Learning in Deductive Databases},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {5},
  number    = {6},
  year      = {1993},
  pages     = {939-949},
  ee        = {db/journals/tkde/DzeroskiL93.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

Most current applications of inductive learning in databases take place in the context of a single extensional relation. The authors place inductive learning in the context of a set of relations defined either extensionally or intentionally in the framework of deductive databases. LINUS, an inductive logic programming system that induces virtual relations from example positive and negative tuples and already defined relations in a deductive database, is presented. Based on the idea of transforming the problem of learning relations to attribute-value form, several attribute-value learning systems are incorporated. As the latter handle noisy data successfully, LINUS is able to learn relations from real-life noisy databases. The use of LINUS for learning virtual relations is illustrated, and a study of its performance on noisy data is presented.

Copyright © 1993 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.


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BibTeX
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Contents BibTeX

Referenced by

  1. Beat Wüthrich: Probabilistic Knowledge Bases. IEEE Trans. Knowl. Data Eng. 7(5): 691-698(1995)
  2. Christopher J. Matheus, Philip K. Chan, Gregory Piatetsky-Shapiro: Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng. 5(6): 903-913(1993)
BibTeX
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