The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.

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Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.

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This paper illustrates the application of abstract compilation using multiple incarnations of the domain Prop in deriving type dependencies for logic programs. We illustrate how dependencies can be derived in the presence of both monomorphic and polymorphic type information. Type dependencies generalize the recently proposed notion of directional types as well as the more common notion of groundness dependencies. Directional types have proven useful in a number of applications such as in proving termination. These applications, however, are based on type declarations. The main contribution of this paper is in the simplicity in which non-trivial type dependencies are inferred using abstract compilation and by associating each type with an incarnation of Prop. We illustrate the use of a semantics for open logic programs in maintaining space e cient analyses. Time e ciency is also maintained due to approximation of the type domain in a boolean lattice calling on results of universal algebra.