Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases

Several activities related to semantically annotated resources can be enabled by a notion of similarity, spanning from clustering to retrieval, matchmaking and other forms of inductive reasoning. We propose the definition of a family of semi-distances over the set of objects in a knowledge base which can be used in these activities. In the line of works on distance-induction on clausal spaces, the family is parameterized on a committee of concepts expressed with clauses. Hence, we also present a method based on the idea of simulated annealing to be used to optimize the choice of the best concept committee.

[1]  Shusaku Tsumoto,et al.  A knowledge-oriented clustering technique based on rough sets , 2001, 25th Annual International Computer Software and Applications Conference. COMPSAC 2001.

[2]  Pavel Zezula,et al.  Similarity Search: The Metric Space Approach (Advances in Database Systems) , 2005 .

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  Nicola Fanizzi,et al.  An Exhaustive Matching Procedure for the Improvement of Learning Efficiency , 2003, ILP.

[5]  Gerhard Widmer,et al.  Machine Learning: ECML-97 , 1997, Lecture Notes in Computer Science.

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

[7]  Shan-Hwei Nienhuys-Cheng.  Distances and Limits on Herbrand Interpretations , 1998, ILP.

[8]  Maurice Bruynooghe,et al.  A Framework for Defining Distances Between First-Order Logic Objects , 1998, ILP.

[9]  Dietrich Wettschereck,et al.  Relational Instance-Based Learning , 1996, ICML.

[10]  Pavel Zezula,et al.  Similarity Search - The Metric Space Approach , 2005, Advances in Database Systems.

[11]  Ashwin Srinivasan,et al.  Mutagenesis: ILP experiments in a non-determinate biological domain , 1994 .

[12]  Luc De Raedt,et al.  kFOIL: Learning Simple Relational Kernels , 2006, AAAI.

[13]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[14]  Michèle Sebag,et al.  Distance Induction in First Order Logic , 1997, ILP.

[15]  Gilles Bisson,et al.  Learning in FOL with a Similarity Measure , 1992, AAAI.

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

[17]  Nicola Fanizzi,et al.  Incremental learning and concept drift in INTHELEX , 2004, Intell. Data Anal..

[18]  Alan Hutchinson,et al.  Metrics on Terms and Clauses , 1997, ECML.

[19]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.