Context-Aware Basic Level Concepts Detection in Folksonomies

Abstract

This paper deals with the problem of exploring implicit semantics in folksonomies. In folksonomies, users create and manage tags to annotate web resources. The collection of user-created tags in folksonomies is a potential semantics source. Much research has been done to extract concepts, and even concepts hierarchy (ontology), which is the important component for knowledge representation (e.g. in semantic web and agent communication), from folksonomies. However, there has been no metric for discovering human acceptable and agreeable concepts, and thus many concepts extracted from folksonomies by existing approaches are not natural for human use. In cognitive psychology, there is a family of concepts named basic level concepts which are frequently used by people in daily life, and most human knowledge is organized by basic level concepts. Thus, extracting basic level concepts from folksonomies is more meaningful for categorizing and organizing web resources than extracting concepts in other granularity. In addition, context plays an important role in basic level concepts detection, as the basic level concepts in the same domain become different in different contexts. In this paper, we propose a method to detect basic level concepts in different contexts from folksonomies. Using Open Directory Project (ODP) as the benchmark, we demonstrate the existence of context effect and the effectiveness of our method.