Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for “Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space.” It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.
SLIPCASE performs a beam search in the space of theories, starting from a trivial LPAD and using the LL of the data as the guiding heuristics. The starting theory for the beam search is user-defined: a good starting point is a theory composed of one probabilistic clause with empty body of the form for each target predicate, where @math is a tuple of variables. At each step of the search the theory with the highest LL is removed from the beam and a set of refinements is generated and evaluated by means of LL; then they are inserted in order of decreasing LL in the beam. The refinements of the selected theory are constructed according to a language bias based on and declarations in Progol style. Following @cite_12 @cite_30 the admitted refinements are: adding or removing a literal from a clause, adding a clause with an empty body or removing a clause. Beam search ends when one of the following occurs: the maximum number of steps is reached, the beam is empty, the difference between the LL of the current theory and the best previous LL drops below a threshold @math .
@cite_30 @cite_12
2162152259 2165520550
Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling. Ourston, D. and R.J. Mooney, Theory refinement combining analytical and empirical methods, Artificial Intelligence 66 (1994) 273-309. This article describes a comprehensive system for automatic theory (knowledge base) refinement. The system applies to classification tasks employing a propositional Hornclause domain theory. Given an imperfect domain theory and a set of training examples, the approach uses partial and incorrect proofs to identify potentially faulty rules. For each faulty rule, subsets of examples are used to inductively generate a correction. Because the system starts with an approximate domain theory, fewer training examples are generally required to attain a given level of classification accuracy compared to a purely empirical learning system. The system has been tested in two previously explored application domains: recognizing important classes of DNA sequences and diagnosing diseased soybean plants.