Semdrops: A Social Semantic Tagging Approach for Emerging Semantic Data

Abstract

This paper proposes a collective intelligence strategy for emerging semantic data. It presents a combination of social web practices with semantic web technologies to enrich existing web resources with semantic data. The paper introduces a social semantic tagging approach called Semdrops. Semdrops defines a conceptual model which is an extension of the Gruber's tag model where the tag concept is extended to semantic tag. Semdrops is implemented as a Firefox add-on tool that turns the web browser into a collaborative semantic data editor. To validate Semdrops's approach, we conducted an evaluation and usability studies and compared the results with automatic generation methods of semantic data such as DBpedia. The studies demonstrated that Semdrops is an effective and complementary approach to produce adequate semantic data on the Web.