Sanja Vranes

Semantic Web Technologies: Ready for Adoption?

A review of semantic Web use in the EU shows that its related technologies are finding their way into real-world applications. Rather than being a fashionable research issue, the semantic Web is slowly but surely becoming a reality.

Integrating multiple paradigms within the blackboard framework

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. >

Applicability assessment of Semantic Web technologies

The Semantic Web is one of the fastest developing fields within the Information and Communication Technology sector and, as such, under constant examination by scientists and IT professionals. This article aims to provide a better understanding of the applicability of Semantic Web tools and technologies in practice. This aim will be achieved by surveying the recommended and emerging W3C standards, presenting an overview of the state-of-the-art in the Semantic Web research in the European Union, analysing the W3C collection of Case studies and Use Cases, and discussing the extent of adoption of Semantic Web technologies. The overall technology maturity level assessment has shown that Semantic Web technologies are finding their ways into real-world applications, and that, rather than being merely a fashionable research issue, the Semantic Web, slowly but surely, becomes our reality.

Prolog/Rex/spl minus/a way to extend Prolog for better knowledge representation

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. >

Knowledge representation with SOUL

Good knowledge organization is crucial in two cases-in the representation of semantically related information and in the representation of huge quantities of data. The answer to the problem of semantic knowledge representation is given by various knowledge representation techniques used in Artificial Intelligence, while for the representation of large quantities of data, relational databases represent the optimal choice. If we analyze the characteristics important from the knowledge organization point of view, we could say that structure representation provides means for knowledge representation, unique object representation ensures representational efficiency, while representation of bottom-up hierarchies (e.g. indexing scheme in relational databases) brings search efficiency. Furthermore, unique object representation and bottom-up (contextual) hierarchies provide support for semantically organized knowledge. In this paper we describe a knowledge representation technique, Hierarchical Semantic Form (HSF), which, together with the Space Of Universal Links (SOUL) algorithm, aims at supporting both of the above mentioned characteristics of well organized knowledge. HSF represents a hybrid solution that enables both structure and context representations, combining the characteristics of classicist approach (symbolic and semantic processing) with the characteristics of connectionist approach (parallelism, learning).

A blackboard framework on top of prolog

Abstract BEST (Blackboard Expert Systems Toolkit) aimed to provide the ability to produce large-scale evolvable, heterogeneous intelligent systems. Apart from providing all necessary blackboard infrastructure and control capabilities, BEST allows its user to integrate different programming paradigms within a single blackboard application. It does not matter if one knowledge source is a forward- or backward-chaining rule-based system, another uses model-based reasoning paradigm, and still another is a conventional, procedural program. Moreover, because representational flexibility is similarly important in blackboard systems, and the blackboard model does not place any prior restrictions on what type of information can be placed on the blackboard, the authors offer multiparadigm knowledge representation language, built on top of Prolog, therefore avoiding restricting users to a single way of expressing either knowledge or data. BESTs problem-solving facilities include assumption-based truth maintenance, temporal, hypothetical, and approximate reasoning. The generality and flexibility of the BEST environment present the first-time blackboard application developer with numerous implementation choices, providing a wide range of options and capabilities,what significantly increases the productivity of BEST programmers, and improves the performance of the application they produce. The ability to explain its reasoning and to defend its decision also distinguished BEST from most other blackboard systems.

Ontology-based facility data model for energy management

Context: Definition of a comprehensive facility data model is a prerequisite for providing more advanced energy management systems capable of tackling the underlying heterogeneity of complex infrastructures, thus providing more flexible data interpretation and event management, advanced communication and control system capabilities. Objective: This paper proposes one of the possible implementations of a facility data model utilizing the concept of ontology as part of the contemporary Semantic Web paradigm. Method: The proposed facility ontology model was defined and developed to model all the static knowledge (such as technical vendor data, proprietary data types, and communication protocols) related to the significant energy consumers of the target infrastructure. Furthermore, this paper describes the overall methodology and how the common semantics offered by the ontology were utilized to improve the interoperability and energy management of complex infrastructures. Initially, a core facility ontology, which represents the generic facility model providing the general concepts behind the modelling, was defined. Results: In order to develop a full-blown model of the specific facility infrastructure, Malpensa and Fiumicino airports in Italy were taken as a test-bed platform in order to develop the airport ontology owing to the variety of the technical systems installed at the site. For the development of the airport ontology, the core facility ontology was first extended and then populated to reflect the actual state of the target airport facility. Conclusion: The developed ontology was tested in the environment of the two pilots, and the proposed solution proved to be a valuable link between separate ICT systems involving equipment from various vendors, both on syntax and semantic level, thus offering the facility managers the ability to retrieve high-level information regarding the performance of significant energy consumers.

Energy hub modelling and optimisation: an analytical case-study

Energy hub modelling and analysis has garnered a significant degree of attention in the last few years, with different approaches and formalisms being proposed to provide a unified framework for dealing with such systems. The present work is derived from one such approach and presents a numerical analysis of a real world case-study based on the Bilbao Exhibition Centre. The modelling and optimisation method is employed to assess the economic profitability of upgrading the system by expanding its portfolio of energy assets and evaluating the impact over a number of years of operation.

Semantic Classifier for Affective Computing

One of the most important fields of affective computing is related to the hard problem of emotion recognition. At present, there are several approaches to the problem of automatic emotion recognition based on different methods, like Bayesian classifiers, support vector machines, linear discriminant analysis, neural networks or k-nearest neighbors, which classify emotions using several features obtained from facial expressions, body gestures, speech or different physiological signals. In this paper, we propose a semantic classifier as a new, simple and efficient approach to the problem of automatic emotion recognition. The implementation of the semantic classifier is based on the basic, natural principles used to decrease the complexity of problems found in n-dimensional spaces: discretization, structure identification and semantic optimization. The proposed classifier exhibits some self-organizing features and supports learning by repetition, generalization and specialization. It will be used to implement a distributed and robust system for emotion recognition.

Decision Aid for Sustainable Industrial Siting

For sustainable industrial development, the need of the hour is judicious, reasonable and planned use of the finite resources of land, according to the natural environmental properties. To cater to this need, we are using geographic information systems and spatial decision support systems techniques to help proper siting of newly planned industries and industrial estates. This software tool is especially meaningful in developing and transition economy countries, where the process of sustainable industrialization is still in its initial stage. The authentic spatial decision making algorithm, as well as underlying software architecture is presented in detail in this paper