An Approach to Predicate Invention Based on Statistical Relational Model

Predicate Invention is the branch of symbolic Machine Learning aimed at discovering new emerging concepts in the available knowledge. The outcome of this task may have important consequences on the efficiency and effectiveness of many kinds of exploitation of the available knowledge. Two fundamental problems in Predicate Invention are how to handle the combinatorial explosion of candidate concepts to be invented, and how to select only those that are really relevant. Due to the huge number of candidates, there is a need for automatic techniques to assign a degree of relevance to the various candidates and select the best ones. Purely logical approaches may be too rigid for this purpose, while statistical solutions may provide the required flexibility.

[1]  Nicola Fanizzi,et al.  Multistrategy Theory Revision: Induction and Abduction in INTHELEX , 2004, Machine Learning.

[2]  Stephen Muggleton,et al.  Predicate invention and utilization , 1994, J. Exp. Theor. Artif. Intell..

[3]  Stanley Kok,et al.  Toward Statistical Predicate Invention , 2006 .

[4]  Pat Langley,et al.  Improving Efficiency by Learning Intermediate Concepts , 1989, IJCAI.

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

[6]  Letizia Tanca,et al.  Logic Programming and Databases , 1990, Surveys in Computer Science.

[7]  Lyle H. Ungar,et al.  Cluster-based concept invention for statistical relational learning , 2004, KDD.

[8]  Thomas L. Griffiths,et al.  Learning Systems of Concepts with an Infinite Relational Model , 2006, AAAI.

[9]  Michael J. Pazzani,et al.  Relational Clichés: Constraining Induction During Relational Learning , 1991, ML.

[10]  Pedro M. Domingos,et al.  Statistical predicate invention , 2007, ICML '07.

[11]  Mark Craven,et al.  Relational Learning with Statistical Predicate Invention: Better Models for Hypertext , 2001, Machine Learning.

[12]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[13]  Christopher Ré,et al.  Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS , 2011, Proc. VLDB Endow..

[14]  Pedro M. Domingos,et al.  Efficient Weight Learning for Markov Logic Networks , 2007, PKDD.

[15]  J. W. Lloyd,et al.  Foundations of logic programming; (2nd extended ed.) , 1987 .

[16]  Donato Malerba,et al.  A Logic Framework for the Incremental Inductive Synthesis of Datalog Theories , 1997, LOPSTR.

[17]  Nir Friedman,et al.  Learning Hidden Variable Networks: The Information Bottleneck Approach , 2005, J. Mach. Learn. Res..

[18]  Foster J. Provost,et al.  Aggregation-based feature invention and relational concept classes , 2003, KDD '03.