Inductive Learning in Deductive Databases.

Saso Dzeroski, Nada Lavrac: Inductive Learning in Deductive Databases. IEEE Trans. Knowl. Data Eng. 5(6): 939-949(1993)
  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,}


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.

Joint ACM SIGMOD / IEEE Computer Society Anthology

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Bojan Cestnik, Igor Kononenko, Ivan Bratko: ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users. EWSL 1987: 31-45 BibTeX
Peter Clark, Robin Boswell: Rule Induction with CN2: Some Recent Improvements. EWSL 1991: 151-163 BibTeX
Peter Clark, Tim Niblett: The CN2 Induction Algorithm. Machine Learning 3: 261-283(1989) BibTeX
Saso Dzeroski, Nada Lavrac: Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. ML 1991: 399-402 BibTeX
William J. Frawley, Gregory Piatetsky-Shapiro, Christopher J. Matheus: Knowledge Discovery in Databases: An Overview. AI Magazine 13(3): 57-70(1992) BibTeX
Nada Lavrac, Saso Dzeroski: Background Knowledge and Declarative Bias in Inductive Concept Learning. AII 1992: 51-71 BibTeX
Nada Lavrac, Saso Dzeroski, Marko Grobelnik: Learning Nonrecursive Definitions of Relations with LINUS. EWSL 1991: 265-281 BibTeX
John W. Lloyd: Foundations of Logic Programming, 2nd Edition. Springer 1987, ISBN 3-540-18199-7
Ryszard S. Michalski, Igor Mozetic, Jiarong Hong, Nada Lavrac: The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains. AAAI 1986: 1041-1047 BibTeX
Igor Mozetic: Learning of Qualitative Models. EWSL 1987: 201-217 BibTeX
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Stephen Muggleton, Cao Feng: Efficient Induction of Logic Programs. ALT 1990: 368-381 BibTeX
Michael J. Pazzani, Dennis F. Kibler: The Utility of Knowledge in Inductive Learning. Machine Learning 9: 57-94(1992) BibTeX
J. Ross Quinlan: Induction of Decision Trees. Machine Learning 1(1): 81-106(1986) BibTeX
J. Ross Quinlan: Simplifying Decision Trees. International Journal of Man-Machine Studies 27(3): 221-234(1987) BibTeX
J. Ross Quinlan: Learning Logical Definitions from Relations. Machine Learning 5: 239-266(1990) BibTeX
Jeffrey D. Ullman: Principles of Database and Knowledge-Base Systems, Volume I. Computer Science Press 1988, ISBN 0-7167-8158-1
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)
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