The learnability of description logics with equality constraints

Although there is an increasing amount of experimental research on learning concepts expressed in first-order logic, there are still relatively few formal results on the polynomial learnability of first-order representations from examples. Most previous analyses in the pac-model have focused on subsets of Prolog, and only a few highly restricted subsets have been shown to be learnable. In this paper, we will study instead the learnability of the restricted first-order logics known as “description logics”, also sometimes called “terminological logics” or “KL-ONE-type languages”. Description logics are also subsets of predicate calculus, but are expressed using a different syntax, allowing a different set of syntactic restrictions to be explored. We first define a simple description logic, summarize some results on its expressive power, and then analyze its learnability. It is shown that the full logic cannot be tractably learned. However, syntactic restrictions exist that enable tractable learning from positive examples alone, independent of the size of the vocabulary used to describe examples. The learnable sublanguage appears to be incomparable in expressive power to any subset of first-order logic previously known to be learnable.

[1]  John F. Sowa,et al.  Principles of semantic networks , 1991 .

[2]  AngluinDana Learning regular sets from queries and counterexamples , 1987 .

[3]  Peter Idestam-Almquist,et al.  Generalization under Implication by Recursive Anti-unification , 1993, ICML.

[4]  Chidanand Apté,et al.  Organizing Knowledge in a Complex Financial Domain , 1987, IEEE Expert.

[5]  Nicholas V. Findler,et al.  Associative Networks- Representation and Use of Knowledge by Computers , 1980, CL.

[6]  Premkumar T. Devanbu,et al.  LaSSIE: a knowledge-based software information system , 1990, [1990] Proceedings. 12th International Conference on Software Engineering.

[7]  H. Hirsh Incremental Version-Space Merging: A General Framework for Concept Learning , 1990 .

[8]  A. R. Brown,et al.  Program Debugging , 1973 .

[9]  Ronald L. Rivest,et al.  Learning decision lists , 2004, Machine Learning.

[10]  Janice I. Glasgow,et al.  Spatial Analogy and Subsumption , 1992, ML.

[11]  J. R. Quinlan Learning Logical Definitions from Relations , 1990 .

[12]  Alexander Borgida Description Logics are not just for the Flightless-Birds: A New Look at the Utility and Foundations , 1992 .

[13]  Stephen Muggleton,et al.  Machine Invention of First Order Predicates by Inverting Resolution , 1988, ML.

[14]  Jörg-Uwe Kietz,et al.  Some Lower Bounds for the Computational Complexity of Inductive Logic Programming , 1993, ECML.

[15]  Frank Pfenning,et al.  Unification and anti-unification in the calculus of constructions , 1991, [1991] Proceedings Sixth Annual IEEE Symposium on Logic in Computer Science.

[16]  Thomas G. Dietterich,et al.  Learning and Inductive Inference , 1982 .

[17]  M. Kearns,et al.  Recent Results on Boolean Concept Learning , 1987 .

[18]  Alexander Borgida,et al.  On the relationship between description logic and predicate logic queries , 1994, CIKM '94.

[19]  Leslie G. Valiant,et al.  Cryptographic Limitations on Learning Boolean Formulae and Finite Automata , 1993, Machine Learning: From Theory to Applications.

[20]  David Page,et al.  Generalization with Taxonomic Information , 1990, AAAI.

[21]  Katharina Morik,et al.  A bootstrapping approach to conceptual clustering , 1989, ICML 1989.

[22]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[23]  Stephen Muggleton,et al.  Efficient Induction of Logic Programs , 1990, ALT.

[24]  Melissa P. Chase,et al.  On Analytical and Similarity-Based Classification , 1990, AAAI.

[25]  Saso Dzeroski,et al.  PAC-learnability of determinate logic programs , 1992, COLT '92.

[26]  Manfred K. Warmuth,et al.  Learning nested differences of intersection-closed concept classes , 2004, Machine Learning.

[27]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, CACM.

[28]  William W. Cohen Cryptographic Limitations on Learning One-Clause Logic Programs , 1993, AAAI.

[29]  Barr and Feigenbaum Edward A. Avron The Handbook of Artificial Intelligence , 1981 .

[30]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[31]  David Haussler,et al.  Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension , 1986, STOC '86.

[32]  David Haussler,et al.  Learning Conjunctive Concepts in Structural Domains , 1989, Machine Learning.

[33]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[34]  Leslie G. Valiant,et al.  Computational limitations on learning from examples , 1988, JACM.

[35]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[36]  N. Littlestone Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[37]  Alexander Borgida,et al.  Computing Least Common Subsumers in Description Logics , 1992, AAAI.

[38]  Leonard Pitt,et al.  Prediction-Preserving Reducibility , 1990, J. Comput. Syst. Sci..

[39]  Ronald J. Brachman,et al.  ON THE EPISTEMOLOGICAL STATUS OF SEMANTIC NETWORKS , 1979 .

[40]  Krzysztof R. Apt,et al.  Logic Programming , 1990, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.

[41]  Daniel G. Bobrow,et al.  On Overview of KRL, a Knowledge Representation Language , 1976, Cogn. Sci..

[42]  Robert Mac Gregor,et al.  THE EVOLVING TECHNOLOGY OF CLASSIFICATION-BASED KNOWLEDGE REPRESENTATION SYSTEMS , 1991 .

[43]  James G. Schmolze,et al.  The KL-ONE family , 1992 .

[44]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[45]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[46]  H. Hirsh Incremental version-space merging , 1990, ICML 1990.

[47]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[48]  Balas K. Natarajan,et al.  On learning Boolean functions , 1987, STOC.

[49]  Ranan B. Banerji,et al.  Learning theories in a subset of a polyadic logic , 1988, COLT '88.

[50]  Klaus-Dieter Schewe,et al.  Variant Construction Using Constraint Propagation Techniques over Semantic Networks , 1989, ÖGAI.

[51]  Sunit K. Gala,et al.  Classification as a query processing technique in the CANDIDE semantic data model , 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.

[52]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[53]  Frank Pfenning,et al.  Uniication and Anti-uniication in the Calculus of Constructions , 1991 .

[54]  Wray L. Buntine Generalized Subsumption and Its Applications to Induction and Redundancy , 1986, Artif. Intell..

[55]  Peter F. Patel-Schneider,et al.  A Semantics and Complete Algorithm for Subsumption in the CLASSIC Description Logic , 1993, J. Artif. Intell. Res..

[56]  David Page,et al.  Learning Constrained Atoms , 1991, ML.

[57]  Thomas G. Dietterich,et al.  A Comparative Review of Selected Methods for Learning from Examples , 1983 .

[58]  Ehud Shapiro,et al.  Algorithmic Program Debugging , 1983 .

[59]  Avrim Blum Learning boolean functions in an infinite attribute space , 1990, STOC '90.

[60]  守屋 悦朗,et al.  J.E.Hopcroft, J.D. Ullman 著, "Introduction to Automata Theory, Languages, and Computation", Addison-Wesley, A5変形版, X+418, \6,670, 1979 , 1980 .

[61]  M R Quillian,et al.  Word concepts: a theory and simulation of some basic semantic capabilities. , 1967, Behavioral science.