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10.5555/3000850.3000877guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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Applications of a logical discovery engine

Published: 31 July 1994 Publication History

Abstract

The clausal discovery engine CLAUDIEN is presented. CLAUDIEN discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larger-than that of attribute value representation, CLAUDIEN also accepts as input a declarative specification of the language bias, which determines the set of syntactically well-formed regularities.
Whereas other papers on CLAUDIEN focuss on the semantics or logical problem specification of CLAUDIEN, on the discovery algorithm, or the PAC-learning aspects, this paper wants to illustrate the power of the resulting technique. In order to achieve this aim, we show how CLAUDIEN can be used, to learn 1) integrity constraints in databases, 2) functional dependencies and determinations, 3) properties of sequences, 4) mixed quantitative and qualitative laws, 5) reverse engineering, and 6) classification rules.

References

[1]
H. Adé, L. De Raedt, and M. Bruynooghe. Declarative Bias for Bottom Up ILP Learning Systems, 1994. Submitted to Machine Learning.
[2]
F. Beigadano and D. Gunetti. An interactive system to learn functional logic programs. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1044-1649. Morgan Kaufmann, 1993.
[3]
W. Cohen. Grammatically biased learning: learning logic programs using an explicit antecedent description language. Artificial Intelligence, 1994. To appear.
[4]
L. De Raedt. Interactive Theory Revision: an Inductive Logic Programming Approach, Academic Press, 1992.
[5]
L. De Raedt and M. Bruynooghe. A theory of clausal discovery. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1058-1063. Morgan Kaufmann, 1993.
[6]
L. De Raedt and S. Džeroski. First order jk clausal theories are PAC-learnable. Technical Report KUL-CW-, Department of Computer Science, Katholieke Universiteit Leuven, 1993. submitted to Artificial Intelligence.
[7]
L. De Raedt and N. Lavrač. The many faces of inductive logic programming. In J. Komorowski, editor, Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems, Lecture Notes in Artificial Intelligence. Springer-Verlag, 1993. invited paper.
[8]
L. De Raedt, N. Lavrač, and S. Džeroski. Multiple predicate learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1037-1042. Morgan Kaufmann, 1993.
[9]
T.G. Dietterich and R.S. Michalski. Discovering patterns in sequences of events. Artificial Intelligence, 25:257-294, 1985.
[10]
B. Dolsak and S. Muggleton. The application of inductive logic programming to finite element mesh design. In S. Muggleton, editor, Inductive logic programming, pages 453-472. Academic Press, 1992.
[11]
S. Džeroski, S. Muggleton, and S. Russel. PAC-learnability of determinate logic programs. In Proceedings of the 5th ACM workshop on Computational Learning Theory, pages 128-135, 1992.
[12]
S. Džeroski and L. Todorovski. Discovering dynamics: from inductive logic programming to machine discovery. In Proceedings of the AAAI'93 Workshop on Knowledge Discovery in Databases, pages 125-137. AAAI Press, 1993. Washington DC.
[13]
P. Flach. Predicate invention in inductive data engineering. In P. Brazdil, editor, Proceedings of the 6th European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, pages 83-94. Springer-Verlag, 1993.
[14]
M. Kantola, H. Mannila, K.J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7), 1992.
[15]
J-U. Kietz and S. Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. In S. Muggleton, editor, Inductive logic programming, pages 335-359. Academic Press, 1992.
[16]
J.U. Kietz. Some lower bounds for the computational complexity of inductive logic programming. In Proceedings of the 6th European Conference on Machine Learning, volume 667, pages 115-124. Lecture Notes in Artificial Intelligence, 1993.
[17]
P. Langley, G.L. Bradshaw, and H.A. Simon. Rediscovering chemistry with the BACON system. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: an artificial intelligence approach, volume 1, pages 307-330. Morgan Kaufmann, 1983.
[18]
N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1993.
[19]
J.W. Lloyd. Foundations of logic programming. Springer-Verlag, 2nd edition, 1987.
[20]
S. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992.
[21]
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 1994. to appear.
[22]
S. Muggleton and C. Feng, Efficient induction of logic programs. In Proceedings of the 1st conference on algorithmic learning theory, pages 368-381. Ohmsma, Tokyo, Japan, 1990.
[23]
G. Piatetsky-Shapiro and W. Prawley, editors. Knowledge discovery in databases. The MIT Press, 1991.
[24]
G. (Ed.) Piatetsky-Shapiro. Special issue on knowledge discovery in databases. International Journal of Intelligent Systems, 7(7), 1992.
[25]
J.R. Quinlan. Learning logical definition from relations. Machine Learning, 5:239-266, 1990.
[26]
I. Savnik and P. A. Flach. Bottom-up induction of functional dependencies from relations. In Proceedings of the AAAI'93 Workshop on Knowledge Discovery in Databases, pages 174-185. AAAI Press, 1993. Washington DC.
[27]
J. Schlimmer. Learning determinations and checking databases. In Proceedings of the AAAI'91 Workshop on Knowledge Discovery in Databases, pages 64-76, 1991. Washington DC.
[28]
E.Y. Shapiro. Algorithmic Program Debugging. The MIT Press, 1983.
[29]
W.M Shen. Discovering regularities from knowledge bases. International Journal of Intelligent Systems, 7(7), 1992.
[30]
W. Van Laer and L. De Raedt. Discovering quantitative laws in inductive logic programming. In Proceedings of the Familiarization Workshop of the ESPRIT Network of Excellence on Machine Learning, pages 8-11, 1993. Extended Abstract, Blanes, Spain.

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cover image Guide Proceedings
AAAIWS'94: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
July 1994
473 pages

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AAAI Press

Publication History

Published: 31 July 1994

Author Tags

  1. deductive databases
  2. first order logic
  3. inductive logic programming
  4. knowledge discovery in databases
  5. machine learning

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