Abstract
The amount of data collected and stored in databases is growing considerably in almost all areas of human activity. In complex applications the data involves several relations and proposionalization is not a suitable approach. Multi-Relational Data Mining algorithms can analyze data from multiple relations, with no need to transform the data into a single table, but are computationally more expensive. In this paper a novel relational classification algorithm based on the k-nearest neighbour algorithm is presented and evaluated.

Abstract
Tabled evaluation has been proved an effective method to improve several aspects of goal-oriented query evaluation, including termination and complexity. Several "native" implementations of tabled evaluation have been developed which offer good performance, but many of them require significant changes to the underlying Prolog implementation, including the compiler and the abstract machine. Approaches based on program transformation, which tend to minimize changes to both the Prolog compiler and the abstract machine, have also been proposed, but they often result in lower efficiency. We explore some techniques aimed at combining the best of these worlds, i.e., developing an extensible implementation which requires minimal modifications to the compiler and the abstract machine, and with reasonably good performance. Our preliminary experiments indicate promising results.

Abstract
Arguably. the most successful data, structure for tabling is tries. However, while tries are very efficient for variant based tabled evaluation, they are limited in their ability to recognize and represent, repeated terms in different tabled calls or/and answers. lit this paper, we propose a new design for the table space where tabled terms are stored in a common global trie instead of being spread over several different tries.

Abstract
The past few years have seen a surge of interest in the field of probabilistic logic learning or 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 mutually independent probabilities that they 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, bind evaluate their performance in the context of large networks of biological entities.

Abstract
Despite the considerable success of Inductive Logic Programming (ILP), deployed ILP systems still have efficiency problems when applied to complex problems. Several techniques have been proposed to address the efficiency issue. Such proposals include query transformations, query packs, lazy evaluation and parallel execution of ILP systems, to mention just a few. We propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique takes advantage of the two stage procedure of Mode Directed Inverse Entailment (MDIE) systems. In the first stage of a MDIE system, where the bottom clause is constructed, we store not only the bottom clause but also valuable additional information. The information stored is sufficient to evaluate the clauses constructed in the second stage without the need for a theorem prover. We used a data structure called Trie to efficiently store all bottom clauses produced using all examples (positive and negative) as seeds. The technique was implemented and evaluated using two well known data sets from the ILP literature. The results are promising both in terms of execution time and accuracy.

Abstract
Inductive Logic Programming (ILP) is a powerful and well-developed abstraction for multi-relational data mining techniques. Despite the considerable success of ILP, deployed ILP systems still have efficiency problems when applied to complex problems. In this paper we propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique is based on the Mode Directed Inverse Entailment approach to ILP, where a bottom clause is generated for each example and the generated clauses are subsets of the literals of such bottom clause. We propose to store in a prefix-tree all clauses that can be generated from all bottom clauses together with some extra information. We show that this information is sufficient to estimate the number of examples that can be deduced from a clause and present an ILP algorithm that exploits this representation. We also present an extension of the algorithm where each prefix-tree is computed only once (compiled) per example. The evaluation of hypotheses requires only basic and efficient operations on trees. This proposal avoids re-computation of hypothesis' value in theory-level search, in cross-validation evaluation procedures and in parameter tuning. Both proposals are empirically evaluated on real applications and considerable speedups were observed.