Approximate Reasoning for Efficient Anytime Induction from Relational Knowledge Bases

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 reasoning technique 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