Abstract
The Semantic Web promises increased precision in automated information sorting, searching, organizing and summarizing. Realizing this requires significantly more reliable meta-information than is readily available for basic human-readable data types today. Relying solely on hand-crafted ontologies and annotation, or solely on artificial intelligence techniques, seems less likely for success than a combination of the two. How this is best done, however, is far from obvious. We propose an intermediate ontological representational level we call SemCards that combines ontological rigour with flexible user interface constructs. SemCards are machine- and human-readable entities that allow non-experts to create and use semantic content with ease, while empowering machines to better assist and participate in the process. We have implemented the SemCard technology on the Semantic Web site Twine.com, which to date has a growing 250k subscribers and over 2 million monthly unique visitors. SemCards allow users to quickly create semantically-grounded data that in turn acts as examples for automation processes, creating a positive iterative feedback loop of metadata creation between user and machine. The result is an increasingly larger, more accurate amount of metadata than with either approach alone. The SemCard provides a holistic solution to the Semantic Web, resulting in powerful management of the full lifecycle of data, including its creation, retrieval, classification, sorting and sharing. Here we present the key ideas behind SemCards and describe the initial implementation of the technology on Twine.com.
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Thórisson, K.R., Spivack, N., Wissner, J.M. (2009). SemCards: A New Representation for Realizing the Semantic Web. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_37
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DOI: https://doi.org/10.1007/978-3-642-04441-0_37
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