Pruning search space for weighted first order horn clause satisfiability

Authors:
Naveen Nair;Anandraj Govindan;Chander Jayaraman;Kiran TVS;Ganesh Ramakrishnan
Affiliations:
IITB-Monash Research Academy, Old CSE Building, IIT Bombay and Department of Computer Science and Engineering, IIT Bombay and Faculty of Information Technology, Monash University;Department of Computer Science and Engineering, IIT Bombay;Department of Computer Science and Engineering, IIT Bombay;Department of Computer Science and Engineering, IIT Bombay;Department of Computer Science and Engineering, IIT Bombay and IITB-Monash Research Academy, Old CSE Building, IIT Bombay
Venue:
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Year:
2010

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Abstract

Many SRL models pose logical inference as weighted satisfiability solving. Performing logical inference after completely grounding clauses with all possible constants is computationally expensive and approaches such as LazySAT [8] utilize the sparseness of the domain to deal with this. Here, we investigate the efficiency of restricting the Knowledge Base (S) to the set of first order horn clauses. We propose an algorithm that prunes the search space for satisfiability in horn clauses and prove that the optimal solution is guaranteed to exist in the pruned space. The approach finds a model, if it exists, in polynomial time; otherwise it finds an interpretation that is most likely given the weights. We provide experimental evidence that our approach reduces the size of search space substantially.