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Social, Structured and Semantic Search

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Abstract

Social content such as blogs, tweets, news etc. is a rich source of interconnected information. We identify a set of requirements for the meaningful exploitation of such rich content, and present a new data model, called S3, which is the first to satisfy them. S3 captures social relationships between users, and between users and content, but also the structure present in rich social content, as well as its semantics. We provide the first top-k keyword search algorithm taking into account the social, structured, and semantic dimensions and formally establish its termination and correctness. Experiments on real social networks demonstrate the efficiency and qualitative advantage of our algorithm through the joint exploitation of the social, structured, and semantic dimensions of S3.
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Dates and versions

hal-01277939 , version 1 (23-02-2016)

Licence

Attribution - NonCommercial - NoDerivatives - CC BY 4.0

Identifiers

  • HAL Id : hal-01277939 , version 1

Cite

Raphaël Bonaque, Bogdan Cautis, François Goasdoué, Ioana Manolescu. Social, Structured and Semantic Search. International Conference on Extending Database Technology, Mar 2016, Bordeaux, France. ⟨hal-01277939⟩
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