Abstract
The content of a Learning Object is frequently characterized by metadata from several standards, such as LOM, SCORM and QTI. Specialized domains require new application profiles that further complicate the task of editing the metadata of learning object since their data models are not supported by existing authoring tools. To cope with this problem we designed a metadata editor supporting multiple metadata languages, each with its own data model. It is assumed that the supported languages have an XML binding and we use RDF to create a common metadata representation, independent from the syntax of each metadata languages. The combined data model supported by the editor is defined as an ontology. Thus, the process of extending the editor to support a new metadata language is twofold: firstly, the conversion from the XML binding of the metadata language to RDF and vice-versa; secondly, the extension of the ontology to cover the new metadata model. In this paper we describe the general architecture of the editor, we explain how a typical metadata language for learning objects is represented as an ontology, and how this formalization captures all the data required to generate the graphical user interface of the editor.

Abstract
Process management systems play an important role for today's information systems. They coordinate the work items among employees and ensure the correct execution of processes. In this paper we focus on the organizational perspective of process management systems. This perspective is responsible for assigning people together with their roles within business organizations to process execution. A key issue in integrating the organizational perspective into processes is the strategy for selecting people to execute work steps. This assignment is the basis for collaboration among the people of an organization within a process-based application. We implemented our approach in ESProNa, a Logtalk application running in SWI-Prolog extended with the Thea library providing direct and complete support for OWL2 ontologies. The use of these languages allows the definition of comprehensive organizational constraints. We will cover both, the definition of these constraints in the process model, and their interpretation by the process execution engine. Further we will show how the organizational perspective impacts the order of process execution, i. e. the way of collaboration between the assigned people decisively. © 2010 ICST.

Abstract
We describe and compare design choices for meta-predicate semantics, as found in representative Prolog module systems and in Logtalk. We look at the consequences of these design choices from a pragmatic perspective, discussing explicit qualification semantics, com- putational reflection support, expressiveness of meta-predicate declara- tions, safety of meta-predicate definitions, portability of meta-predicate definitions, and meta-predicate performance. Our aim is to provide use- ful insight for debating meta-predicate semantics and portability issues based on actual implementations and common usage patterns.

Abstract
In this paper we describe how declarative process modeling together with ontologies can be used to build complex clinical process models. Our approach supports the definition of functional, behavioral, organizational, data and operational process perspectives, resulting in an expressive and flexible modeling language. We use constraints for representing inter-process dependencies and constraint propagation for finding which processes are executable in user selected or given scenarios. Knowledge about the organizational perspective of a clinical ontology can be represented and imported from RDF files for interfacing with other applications. We implemented our approach in ESProNa, a Logtalk application running on SWI-Prolog extended with the CLP(FD) constraint library and the N3 parser Henry.

Abstract
One of the main advantages of Logic Programming (LP) is that it provides an excellent framework for the parallel execution of programs. In this work we investigate novel techniques to efficiently exploit parallelism from real-world applications in low cost multi-core architectures. To achieve these goals, we revive and redesign the YapOr system to exploit or-parallelism based on a multi-threaded implementation. Our new approach takes full advantage of the state-of-the-art fast and optimized YAP Prolog engine and shares the underlying execution environment, scheduler and most of the data structures used to support YapOr's model. Initial experiments with our new approach consistently achieve almost linear speedups for most of the applications, proving itself as a good alternative for exploiting implicit parallelism in the currently available low cost multi-core architectures.

Abstract
Inference in many probabilistic logic systems is based on representing the proofs of a query as a DNF Boolean formula. Assessing the probability of such a formula is known as a #P-hard task. In practice, a large DNF is given to a BDD software package to construct the corresponding BDD. The DNF has to be transformed into the input format of the package. This is the preprocessing step. In this paper we investigate and compare different preprocessing methods, including our new trie based approach. Our experiments within the ProbLog system show that the behaviour of the methods changes according to the amount of sharing in the original DNF. The decomposition method is preferred when there is not much sharing in the DNF, whereas DNFs with sharing benefit from our trie based method. While our methods are motivated and applied in the ProbLog context, our results are interesting for other applications that manipulate DNF Boolean formulae.