Finding the Most Similar Concepts in Two Different Ontologies

Abstract. A concise manner to send information from agent A to B is to use phrases constructed with the concepts of A: to use the concepts as the atomic tokens to be transmitted. Unfortunately, tokens from A are not understood by (they do not map into) the ontology of B, since in general each ontology has its own address space. Instead, A and B need to use a common communication language, such as English: the transmission tokens are English words. An algorithm is presented that finds the concept cB in OB (the ontology of B) most closely resembling a given concept cA. That is, given a concept from ontology OA, a method is provided to find the most similar concept in OB, as well as the similarity sim between both concepts. Examples are given. 1 Introduction an

Implement Web Learning System Based on Genetic Algorithm and Pervasive Agent Ontology

For a web-based dynamic learning environment, personalized support for learners becomes more important. In order to achieve optimal efficiency in a learning process, individual learner's cognitive learning style should be taken into account. It is necessary to provide learners with an individualized learning support system. In this paper, a framework of web learning system based on genetic algorithm and Pervasive Agent Ontology is presented. The proposed framework utilizes genetic algorithm for representing and extracting a dynamic learning process and learning pattern to support students' deep learning in web-based learning environment. Aiming at the problems in current Web environment, we put forward the information integration method of Semantic Web based on Pervasive Agent Ontology (SWPAO method), which will integrate, analyze and process enormous web information and extract answers for students on the basis of semantics. And experiments do prove that it is feasible to use the method to develop an individual Web-based learning system, which is valuable for further study in more depth. ? 2008 Springer-Verlag Berlin Heidelberg.EI

On how to model content engineering in a semantic web environment

The technologies of the Semantic Web demand complex publications which themselves are the result of complex production processes. The complexity of these publications is cause and effect of more sophisticated communication processes possible through the Semantic Web. We introduce a model which briefly describes these processes on a solution-independent level by using a market perspective. The model is based on the assumption of a market interaction between content offer and demand, but is independent of the existence of real content markets with financial transactions. We emphasise the need of structured guidelines for Semantic Web Content Engineering Processes. Furthermore, the model represents a foundation for their development