Semdrops: A Social Semantic Tagging Approach for Emerging Semantic Data

Abstract : 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.
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Communication dans un congrès
2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2011), Aug 2011, Lyon, France. 2011
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Contributeur : Diego Torres <>
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Dernière modification le : jeudi 11 janvier 2018 - 01:52:39
Document(s) archivé(s) le : samedi 10 septembre 2011 - 02:21:18

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  • HAL Id : inria-00599031, version 1

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Diego Torres, Alicia Diaz, Hala Skaf-Molli, Pascal Molli. Semdrops: A Social Semantic Tagging Approach for Emerging Semantic Data. 2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2011), Aug 2011, Lyon, France. 2011. 〈inria-00599031〉

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