Abstract
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.

Abstract
Tabled evaluation is a recognized and powerful technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant subcomputations. We can distinguish two main categories of tabling mechanisms: suspension-based tabling and linear tabling. While suspension-based mechanisms are considered to obtain better results in general, they have more memory space requirements and are more complex and harder to implement than linear tabling mechanisms. Arguably, the SLDT and Dynamic Reordering of Alternatives (DRA) strategies are the two most successful extensions to standard linear tabled evaluation. In this work, we propose a new strategy, named dynamic reordering of solutions, and we present a framework, on top of the Yap system, that supports the combination of all these three strategies. Our implementation shares the underlying execution environment and most of the data structures used to implement tabling in Yap. We thus argue that all these common features allows us to make a first and fair comparison between these different linear tabling strategies and, therefore, better understand the advantages and weaknesses of each, when used solely or combined with the others.

Abstract
Tabled evaluation is an implementation technique that solves some problems of traditional Prolog systems in dealing with recursion and redundant computations. Most tabling engines determine if a tabled subgoal will produce or consume answers by using variant checks. A more refined method, named call subsumption, considers that a subgoal A will consume from a subgoal B if A is subsumed by (an instance of) B, thus allowing greater answer reuse. We recently developed an extension, called Retroactive Call Subsumption, that improves upon call subsumption by supporting bidirectional sharing of answers between subsumed/subsuming subgoals. In this paper, we present both an algorithm and an extension to the table space data structures to efficiently implement instance retrieval of subgoals for subsumptive tabled evaluation of logic programs. Experiments results using the YapTab tabling system show that our implementation performs quite well on some complex benchmarks and is robust enough to handle a large number of subgoals without performance degradation.

Abstract

Abstract
Tabling is an implementation technique that overcomes some limitations of traditional Prolog systems in dealing with redundant sub-computations and recursion. A critical component in the implementation of an efficient tabling system is the design of the table space. The most popular and successful data structure for representing tables is based on a two-level trie data structure, where one trie level stores the tabled subgoal calls and the other stores the computed answers. The Global Trie (GT) is an alternative table space organization designed with the intent to reduce the tables's memory usage, namely by storing terms in a global trie, thus preventing repeated representations of the same term in different trie data structures. In this paper, we propose an extension to the GT organization, named Global Trie for Subterms (GT-ST), where compound subterms in term arguments are represented as unique entries in the GT. Experimental results using the Yap Tab tabling system show that GT-ST support has potential to achieve significant reductions on memory usage, for programs with increasing compound subterms in term arguments, without compromising the execution time for other programs.

Abstract