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The main purpose of this paper is to evaluate the potential of OWL (Web Ontology Language) ontologies for generation of rules. It is assumed that OWL ontology expressions can serve as a material for rule extraction. It is necessary for Semantic Web Expert System development and it is expected that such Semantic Web Expert System (SWES) will be able to process ontologies from the Web with the purpose to supplement or even to develop its knowledge base. Available publications show that the problem of rule extraction from ontologies is not investigated deeply enough.

Evolution of the Concept Map Based Adaptive Knowledge Assessment System: Implementation and Evaluation ResultsThe paper represents the concept map based adaptive knowledge assessment system. Advantages of concept maps are analyzed emphasizing that the approach offers a reasonable balance between requirements to assess higher levels of knowledge according to Bloom's taxonomy and complexity of a system. Concept maps allow revealing of student's knowledge structure, promote system thinking and support process oriented learning where a study course is divided into stages in each of which knowledge assessment is carried out. The developed knowledge assessment system consists from a teacher's, learner's and administrator's modules and is implemented as a multiagent system. Four prototypes of the system developed within four projects are described. The first prototype supports only fill-in-the-map tasks where a learner must put given concepts in correct places. The second prototype provides changing the degree of task difficulty, thus, performing adaptation to a learner's knowledge level. The set of tasks are also extended by construct-the-map tasks. Improvements implemented in the third prototype allow using of directed arcs and standard relationships in concept maps. The three-tier architecture used in the fourth prototype is chosen to rise the security level of the system. Besides that learner's support is considerably expanded giving help and tutoring to a learner. Results of evaluation of the developed system's prototypes in different study courses are presented. The paper concludes with the comparison of all four prototypes using all main characteristics of the developed knowledge assessment system.

-The paper presents the concept map based intelligent knowledge assessment system developed by the Department of Systems Theory and Design of the Faculty of Computer Science and Information Technology of Riga Technical University. The main attention is devoted to the implementation of various kinds of feedback intended for promotion of effective learning and informing of a teacher about students' progress. The feedback is considered from three points of view: feedback generated automatically by the system and given to a student and to a teacher, and feedback provided by a student using questionnaires embedded into the system. All feedback types are discussed in detail and demonstrated using screenshots. Related works are presented as well. The calculation of the concepts' mastering degree and composition of the text summary informing a student about the best and the poorly known concepts within a task is described. The constituent parts of the questionnaire system (the questionnaire designer, the questionnaire filler and the questionnaire reporting system) are specified and all kinds of questions available in the questionnaire designer are demonstrated. The overall functionality of the system is presented paying attention to the goal of the system, scenario of the use and range of provided tasks.

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-The paper presents a conception of the Semantic Web Expert System which is the logical continuation of the expert system development. The Semantic Web Expert System emerges as the result of evolution of expert system concept and it means expert system moving toward the Web and using new Semantic Web technologies. The proposed conception of the Semantic Web Expert System promises to have new useful features that distinguish it from other types of expert systems.

Usage of Graph Patterns for Knowledge Assessment Based on Concept MapsThe paper discusses application of concepts maps (CMs) for knowledge assessment. CMs are graphs which nodes represent concepts and arcs represent relationships between them. CMs reveal learners' knowledge structure and allow assessing their knowledge level. Step-by-step construction and use of CMs is easy. However, mere comparison of expert constructed and learners' completed CMs forces students to construct their knowledge exactly in the same way as experts. At the same time it is known that individuals construct their knowledge structures in different ways. The developed adaptive knowledge assessment system which is implemented as multiagent system includes the knowledge evaluation agent which carries out the comparison of CMs. The paper presents a novel approach to comparison of CMs using graph patterns. Graph patterns are subgraphs, i.e., paths with limited length. Graph patterns are given for both fill-in-the-map tasks where CM structure is predefined and construct-the-map tasks. The corresponding production rules of graph patterns allow to expand the expert's constructed CM and in this way to promote more flexible and adaptive knowledge assessment.