Abstract
One of the advantages of logic programming is the fact that it offers several sources of implicit parallelism. One particularly interesting form of And-Parallelism is Independent And-Parallelism (IAP). Most work on the implementation of IAP is based on Hermenegildo's RAP-WAM. Unfortunately there are some drawbacks associated with the classical approaches based on the use of parcalls and markers. One first observation is that the introduction of parcall frames significantly slows down sequential execution. Moreover, it may result in fine-grained parallel work. We found these problems to be particularly significant in the context of the implementation of combined AND/OR systems. In this paper we take a fresh look at this issue. Our goal is to start from a standard sequential Prolog implementation and try to discover the minimal number of changes that would be required for an efficient implementation of IAP. The key ideas in our design are to (i) to always take advantage of analogy between or-parallelism and IAP; (ii) to avoid creating new structures by adapting preexistingx WAM data-structures wherever possible; and (iii) to avoid major changes to the compiler. The authors would like to acknowledge and thank the contribution and support from Fernando Silva. The work has also benefitted from discussions with Luis Fernando Castro, Ines de Castro Dutra, Kish Shen, Gopal Gupta, and Enrico Pontelli. Our work has been partly supported by Fundaçã da Ciencia e Tecnologia and JNICT under the projects Melodia (JNICT/PBIC/C/TIT/2495/95) and Dolphin (PRAXIS/2/2.l/TIT/1577/95). © 2000 Published by Elsevier B.V.

Abstract
We argue that in order to exploit both Independent And-and Or-parallelism in Prolog programs there is advantage in recomputing some of the independent goals, as opposed to all their solutions being reused. We present an abstract model, called the Composition-tree, for representing and-or parallelism in Prolog programs. The Composition-tree closely mirrors sequential Prolog execution by recomputing some independent goals rather than fully re-using them. We also outline two environment representation techniques for And-Or parallel execution of full Prolog based on the Composition-tree model abstraction. We argue that these techniques have advantages over earlier proposals for exploiting and-or parallelism in Prolog. © 1993 Ohmsha, Ltd. and Springer.

Abstract
A typical classification problem involves building a model to correctly segregate instances of two or more classes. Such a model exhibits differential prediction with respect to given data subsets when its performance is significantly different over these subsets. Driven by a mammography application, we aim at learning rules that predict breast cancer stage while maximizing differential prediction over age-stratified data. In this work, we present the first multi-relational differential prediction (aka uplift modeling) system, and propose three different approaches to learn differential predictive rules within the Inductive Logic Programming framework. We first test and validate our methods on synthetic data, then apply them on a mammography dataset for breast cancer stage differential prediction rule discovery. We mine a novel rule linking calcification to in situ breast cancer in older women. © 2012 Springer-Verlag.

Abstract
"Traditional" clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are "similar" among them and are "dissimilar" to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation. We present a novel conceptual clustering system for multi-relational data, based on the popular k?-?medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster's members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
The rational development of new drugs is a complex and expensive process. A myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognised as the major hurdle behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship studies, using relational Machine Learning algorithms, proved already to be very useful in the complex process of rational drug design. However, a typical problem with those studies concerns the use of available repositories of previously studied molecules. It is quite often the case that those repositories are highly biased since they contain lots of molecules that are similar to each other. This results from the common practice where an expert chemist starts off with a lead molecule, presumed to have some potential, and then introduces small modifications to produce a set of similar molecules. Thus, the resulting sets have a kind of similarity bias. In this paper we assess the advantages of filtering out similar molecules in order to improve the application of relational learners in Structure-Activity Relationship (SAR) problems to predict toxicity. Furthermore, we also assess the advantage of using a relational learner to construct comprehensible models that may be quite valuable to bring insights into the workings of toxicity.

Abstract
The non-portability of Prolog programs is widely considered one of the main problems facing Prolog programmers. Although since 1995, the core of the language is covered by the ISO standard 13211-1, this standard has not been sufficient to support large Prolog applications. As an approach to address this problem, since 2007, YAP and SWI-Prolog have established a basic compatibility framework. The aim of the framework is running the same code on Edinburgh-based Prolog systems rather than having to migrate an application. This article describes the implementation and evaluates this framework by studying how it can be used on a number of libraries and an important application.