The increasing volume of eGovernment-related services is demanding new approaches for service integration and interoperability in this domain. Semantic web (SW) technologies and applications can leverage the potential of eGovernment service integration and discovery, thus tackling the problems of semantic heterogeneity characterizing eGovernment information sources and the different levels of interoperability. eGovernment services will therefore be semantically described in the foreseeable future. In an environment with semantically annotated services, software agents are essential as the entities responsible for exploiting the semantic content in order to automate some tasks, and so enhance the user's experience. In this paper, we present a framework that provides a seamless integration of semantic web services and intelligent agents technologies by making use of ontologies to facilitate their interoperation. The proposed framework can assist in the development of powerful and flexible distributed systems in complex, dynamic, heterogeneous, unpredictable and open environments. Our approach is backed up by a proof-of-concept implementation, where the breakthrough of integrating disparate eGovernment services has been tested.

Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi‐automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language‐independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.

In any modelling activity, a framework to determine the maturity of a developed model before its use is highly advantageous. Such a framework would save modellers expensive time in many areas of information systems. It would also lower the risk of users relying on an incomplete or inaccurate model. In this paper, we develop a framework which uses internal inconsistencies as a quantitative indicator for estimating the completeness and correctness of a model as it is cooperatively evolved. Whilst internal inconsistencies are due to bad fit between different parts of a model, we argue that they are also correlated with how the evolved model fits with the "world". This argument underpins our framework to evaluate integrated models. Contributions of this paper are three folds: firstly, it presents a theoretically grounded framework for integrating models. We extend an existing incremental modelling framework, NRDR, which represents multiple hierarchical restricted domains (MHRD), with automatic concept integration to allow NRDR to deal with multiple experts. Secondly, we couple this integration framework with a theoretically grounded monitoring process to assess the quality of the cooperatively developed model. Thirdly, we illustrate an initial empirical study of our evaluation and integration framework in a computer hardware administration domain. We capture and integrate computer hardware models from several experts and we use our modelling evaluation framework to evaluate the resultant cooperative model.

An ontology can be seen as an approximation to what exists in the real world. An ontological model is the kernel used for specifying ontologies so that how close an ontology can be from the real world depends on the possibilities offered by the ontological model. In this paper, an ontological model that permits the specification of concepts, attributes, mereological and taxonomic relationships, and structural axioms, and that has already been applied to different application domains is extended to contain topological relationships as well. It is demonstrated in this paper that topological relations can be established on ontologies built with that ontological model, and the topological relations are not established by the user but inferred from the knowledge contained in the particular ontology. The domain chosen for this work is the relation between drugs consumption and cancer diagnoses.

This paper presents a new category of social agents in multiple agent systems. This new class of agent, named teleo-social agents, is concerned with both maintenance and protection of the group central goal. The knowledge level of the teleo-social agent consists of a) the components, composition laws, behaviour laws and medium of the group, b) the social tasks the agent can apply to it, and c) a social balance-oriented control model. This type of agent is introduced by referring to its use in simulation of development projects.

Maintenance is a problem with all knowledge based systems, including fuzzy rule based systems. Although the use of fuzzy concepts partially reduces the maintenance problems, it introduces other problems in tuning the numbers used by the system. Ripple Down Rules (RDR) largely overcomes the maintenance problems for conventional crisp systems. The aim of this work was to apply the RDR approach to fuzzy rules to facilitate the maintenance and tuning of the numbers involved. On the other hand, the use of fuzzy concepts in the RDR system would allow the more natural expression of concepts, which is the main advantage of a fuzzy approach. The paper presents a theoretical foundation for combining RDR and fuzzy concepts.