This article describes the Linked Data approach, based on principles defined back in 2006, which can play an important role in the domain of semantic interoperabiity of government services. Therefore, this article explores the technical aspects and challenges of implementation of the revised European Directive on the Public Sector Information (2013/37/EU) that provides a common legal framework for a European market for government-held data (public sector information). It examines how the Linked Data approach facilitates the PSI Directive implementation, and in particular, the maturity of standards and tools for statistical Linked Data processing. The statistical domain has been selected due to its relevance for policy prediction, planning and adjustments, and well as its significant impact on the society, from citizens to businesses to governments. The main contributions are related to the delivered state-of-the-art open-source tools for the managing statistical Linked Data and metadata—quality assessment, exploration—and the recommendations that have been integrated in the EU SHARE-PSI Best Practices collection.

The objective of our decision support system is to provide its users with an independent, objective and credible assessment of both mature and emerging technologies, taking into account aggregated technical, economic, social, strategic, and environmental criteria. It is well known that such holistic technology assessment is a process rather than a set of techniques, and involves consultation and interaction between the scientific community, technology developers, investors and policy makers. Also, it is not a "one-off" process, but is iterative to take account of feedback, new developments and potential social and environmental implications of new technology. Therefore, it is of crucial importance to take advantage of emerging information technologies, especially artificial intelligence and decision support systems, and provide software assistance to this time-consuming and expensive process. The paper describes one effort, aiming at providing such assistance.

The complexity and diversity of real world applications have forced researchers in the AI field to focus more on the integration of diverse knowledge representation and reasoning techniques for solving challenging, real world problems. Our development environment, BEST (Blackboard based Expert Systems Toolkit), is aimed to provide the ability to produce large scale, evolvable, heterogeneous intelligent systems. BEST incorporates the best of multiple programming paradigms in order to avoid restricting users to a single way of expressing either knowledge or data. It combines rule based programming, object oriented programming, logic programming, procedural programming and blackboard modelling in a single architecture for knowledge engineering, so that the user can tailor a style of programming to his application, using any or arbitrary combinations of methods to provide a complete solution. The deep integration of all these techniques yields a toolkit more effective even for a specific single application than any technique in isolation or collections of multiple techniques less fully integrated. Within the basic, knowledge based programming paradigm, BEST offers a multiparadigm language for representing complex knowledge, including incomplete and uncertain knowledge. Its problem solving facilities include truth maintenance, inheritance over arbitrary relations, temporal and hypothetical reasoning, opportunistic control, automatic partitioning and scheduling and both blackboard and distributed problem solving paradigms.>

Prolog/Rex represents a powerful amalgamation of the latest techniques for knowledge representation and processing, rich in semantic features that ease the difficult task of encoding heterogeneous knowledge of real-world applications. The Prolog/Rex concept mechanism lets a user represent domain entities in terms of their structural and behavioral properties, including multiple inheritance, arbitrary user-defined relations among entities, annotated values (demons), incomplete knowledge, etc. A flexible rule language helps the knowledge engineer capture human expertise and provide flexible control of the reasoning process. Additional Prolog/Rex strength that cannot be found in any other hybrid language made on top of Prolog is language level support for keeping many potentially contradictory solutions to a problem, allowing possible solutions and their implications to be automatically generated and completely explored before they are committed. The same mechanism is used to model time-states, which are useful in planning and scheduling applications of Prolog/Rex.>