An intelligent system for semi-automatic evolution of ontologies

Abstract

Ontologies are an important part of the Semantic Web as well as of many intelligent systems. However, the traditional expert-driven development of ontologies is time-consuming and often results in incomplete and inappropriate ontologies. In addition, since ontology evolution is not controlled by end users, it may take too long for a conceptual change in the domain to be reflected in the ontology. In this paper, we present a recommendation algorithm in a Web 2.0 platform that supports end users to collaboratively evolve ontologies by suggesting semantic relations between new and existing concepts. We use the Wikipedia category hierarchy to evaluate our algorithm and our experimental results show that the proposed algorithm produces high quality recommendations.

Personalized Students’ Profile Based On Ontology and Rule-based Reasoning

“…Similarly, Gasevic et al [40] have also used domain ontology for semantically marking up the content of a learning object. In the work of Ramezani et al [41], an algorithm in a Web 2.0 platform is recommended that supports end users collaboratively to evolve ontologies by suggesting semantic relations between new and existing concepts. They use the Wikipedia category hierarchy to evaluate the algorithm and the experimental results show that it produces high quality recommendations.…”