Abstract
In this paper we present the work in progress on LogCHEM, an ILP based tool for discriminative interactive mining of chemical fragments. In particular, we describe the integration with a molecule visualisation software that allows the chemist to graphically control the search for interesting patterns in chemical fragments. Furthermore, we show how structured information, such as rings, functional groups like carboxyl, amine, methyl, ester, etc are integrated and exploited in LogCHEM.

Abstract
We present a novel approach to cluster sets of protein sequences, based on Inductive Logic Programming (ILP). Preliminary results show that; the method proposed Produces understand able descriptions/explanations of the clusters. Furthermore, it can be used as a knowledge elicitation tool to explain clusters proposed by other clustering approaches, such as standard phylogenetic programs.

Abstract
Prolog is traditionally not statically typed. Since the benefits of static typing are huge it was decided to grow a. portable type system inside two widely used open source Prolog systems: SWI-Prolog and Yap. This requires close cooperation and agreement, between the two systems. The type system is Hindley-Milner. The main characteristics of the introduction of types in SWI and Yap are that, typing is not mandatory, that typed and untyped code call be mixed, and that the type checker call insert dynamic type checks at the boundaries between typed and untyped code. The basic decisions and the current status of Hie Typed Prolog project are described. as well as the remaining tasks and problems to be solved.

Abstract
Logic Programming and the Prolog language have a major role in Computing. Prolog, and its derived languages, have been widely used in a impressive variety of application domains. Thus, a bit of the history of Logic Programming reflects in the history of systems such as Dec-10 Prolog [32], M-Prolog [15], C-Prolog [19], Quintus Prolog [20], SICStus Prolog [6], BIM-Prolog [17], ECLiPSe [1], BinProlog [30], SWI-Prolog [34], CIAO [14], and B-Prolog [35], to mention but a few. I briefly present the evolution of one such system, YAP, and present a personal perspective on the challenges ahead for YAP (and for Logic Programming). © 2008 Springer Berlin Heidelberg.

Abstract
First-Order Theory Revision from Examples is the process of improving user-defined or automatically generated First-Order Logic (FOL) theories, given a set of examples. So far, the usefulness of Theory Revision systems has been limited by the cost of searching the huge search spaces they generate. This is a general difficulty when learning FOL theories but recent work showed that Stochastic Local Search (SLS) techniques may be effective, at least when learning FOL theories from scratch. Motivated by these results, we propose novel SLS based search strategies for First-Order Theory Revision from Examples. Experimental results show that introducing stochastic search significantly speeds up the runtime performance and improve accuracy.

Abstract
Learning from multi-relational domains has gained increasing attention over the past few years. Inductive logic programming (ILP) systems, which often rely on hill-climbing heuristics in learning first-order concepts, have been a dominating force in the area of multi-relational concept learning. However, hill-climbing heuristics are susceptible to local maxima and plateaus. In this paper, we show how we can exploit the links between objects in multi-relational data to help a first-order rule learning system direct the search by explicitly traversing these links to find paths between variables of interest. Our contributions are twofold: (i) we extend the pathfinding algorithm by Richards and Mooney [12] to make use of mode declarations, which specify the mode of call (input or output) for predicate variables, and (ii) we apply our extended path finding algorithm to saturated bottom clauses, which anchor one end of the search space, allowing us to make use of background knowledge used to build the saturated clause to further direct search. Experimental results on a medium-sized dataset show that path finding allows one to consider interesting clauses that would not easily be found by Aleph. © Springer-Verlag Berlin Heidelberg 2005.