Using Concept Maps and Fuzzy Set Information Retrieval Model to Dynamically Personalize RSS Feeds

Abstract

Knowledge has always been at the heart of work and action of people. The amount of knowledge is growing and types of knowledge are becoming more diverse. Effective knowledge management may require going beyond basic knowledge capture. Recent work has focused on enhancing knowledge capturing and codification on the web as it is becoming the dominant source of knowledge. Semantic web and Web 2.0 have opened additional opportunities for knowledge management with technologies and tools such as topic maps and concept maps. However, using such technologies imposed many challenges because of the knowledge codification and generation tools such as blogs, wikis, forums, really simple syndicate (RSS), social tagging and others. This research provides a novel method for instantly updating web users with pieces of knowledge tailored to their personal knowledge interests from RSS feeds by combining fuzzy set information retrieval model with the semantics and structure of concept maps. Experiments show that the proposed method can achieve precision levels ranging between 80% and 100%. In addition, the method is robust and has a higher level of agreement with expert judges than the agreement within judges.