Hybrid Learning of Ontology Classes

Description logics have emerged as one of the most successful formalisms for knowledge representation and reasoning. They are now widely used as a basis for ontologies in the Semantic Web. To extend and analyse ontologies, automated methods for knowledge acquisition and mining are being sought for. Despite its importance for knowledge engineers, the learning problem in description logics has not been investigated as deeply as its counterpart for logic programs. We propose the novel idea of applying evolutionary inspired methods to solve this task. In particular, we show how Genetic Programming can be applied to the learning problem in description logics and combine it with techniques from Inductive Logic Programming. We base our algorithm on thorough theoretical foundations and present a preliminary evaluation.

[1]  Shan-Hwei Nienhuys-Cheng,et al.  Foundations of Inductive Logic Programming , 1997, Lecture Notes in Computer Science.

[2]  Luigi Iannone,et al.  Knowledge-Intensive Induction of Terminologies from Metadata , 2004, SEMWEB.

[3]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[4]  Jeffrey M. Bradshaw,et al.  Applying KAoS Services to Ensure Policy Compliance for Semantic Web Services Workflow Composition and Enactment , 2004, SEMWEB.

[5]  Patricia Riddle,et al.  Evolution of logic programs: part-of-speech tagging , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Federico Divina,et al.  Evolutionary Concept Learning , 2002, GECCO.

[7]  Liviu Badea,et al.  A Refinement Operator for Description Logics , 2000, ILP.

[8]  John R. Koza,et al.  Genetic Programming IV: Routine Human-Competitive Machine Intelligence , 2003 .

[9]  Stephen Muggleton,et al.  A Genetic Algorithms Approach to ILP , 2002, ILP.

[10]  Pat Langley,et al.  Editorial: On Machine Learning , 1986, Machine Learning.

[11]  Federico Divina,et al.  Evolutionary concept learning in First Order Logic: An overview , 2006, AI Commun..

[12]  Jukka Hekanaho Background Knowledge in GA-based Concept Learning , 1996, ICML.

[13]  J. Koza,et al.  A Genetic Programming Tutorial , 2003 .

[14]  William W. Cohen,et al.  Learning the Classic Description Logic: Theoretical and Experimental Results , 1994, KR.

[15]  Luigi Iannone,et al.  An algorithm based on counterfactuals for concept learning in the Semantic Web , 2005, Applied Intelligence.

[16]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[17]  Raymond J. Mooney,et al.  Automated refinement of first-order horn-clause domain theories , 2005, Machine Learning.

[18]  Filippo Neri,et al.  Analysis of Genetic Algorithms Evolution under Pure Selection , 1995, ICGA.

[19]  Stephen Muggleton,et al.  Inverse entailment and progol , 1995, New Generation Computing.

[20]  Moonis Ali,et al.  Innovations in Applied Artificial Intelligence , 2005 .

[21]  Gilles Venturini,et al.  Learning First Order Logic Rules with a Genetic Algorithm , 1995, KDD.

[22]  Stephen Muggleton,et al.  Searching the Subsumption Lattice by a Genetic Algorithm , 2000, ILP.

[23]  Jens Lehmann,et al.  Foundations of Refinement Operators for Description Logics , 2007, ILP.

[24]  Cosimo Anglano,et al.  An Experimental Evaluation of Coevolutive Concept Learning , 1998, ICML.

[25]  Raymond J. Mooney,et al.  Automated refinement of first-order horn-clause domain theories , 2005, Machine Learning.

[26]  Kwong-Sak Leung,et al.  Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System , 1995, IEEE Expert.

[27]  Thomas G. Dietterich,et al.  Readings in Machine Learning , 1991 .

[28]  David Haussler,et al.  Occam's Razor , 1987, Inf. Process. Lett..