Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Nonrecursive Definitions of Relations with LINUS
EWSL '91 Proceedings of the European Working Session on Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Dynamic Approach to Dimensionality Reduction in Relational Learning
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
RSD: relational subgroup discovery through first-order feature construction
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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In most real-world applications the choice of the right representation language represents a fundamental issue, since it may give opportunities for generalization and make inductive reasoning computationally easier or harder. While the setting of First Order Logic (FOL) is the most suitable one to model the multi-relational data of real and complex domains, on the other hand it puts the question of the computational complexity of the knowledge induction that represents a challenge for multi-relational data mining algorithms. Indeed, the complexity of most real domains, in which a lot of relationships are required to model the objects involved, calls for both an efficient and effective search method for exploring the space of candidate solutions and a deduction procedure assessing the validity of the discovered knowledge. A way of tackling the complexity of such domains is to use a method that reformulates a multi-relational learning task into an attribute-value one. In this paper we propose an approximate reasoningtechnique that decreases the complexity of a relational problem changing both the language and the inference operation used for the deduction. The complexity of the FOL language is decreased by means of a stochastic propositionalization method, while the NP-completeness of the deduction is tackled using an approximate query evaluation. The induction is performed with an anytime algorithm, implemented by a population based method, able to efficiently extract knowledge from structured data in form of complete FOL definitions. The validity of the proposed technique has been proved making an empirical evaluation on a real-world dataset.