SEMashup: Making Use of Linked Data for Generating Enhanced Snippets

Abstract : We enhance an existing search engine’s snippet (i.e. excerpt from a web page determined at query-time in order to efficiently express how the web page may be relevant to the query) with linked data (LD) in order to highlight non trivial relationships between the information need of the user and LD resources related to the result page. To do this, we introduce a multi-step unsupervised co-clustering algorithm so as to use the textual data associated with the resources for discovering additional relationships. Next, we use a 3-way tensor to mix these new relationships with the ones available from the LD graph. Then, we apply a first PARAFAC tensor decomposition [5] in order to (i) select the most promising nodes for a 1-hop extension, and (ii) build the enhanced snippet.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01301087
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Submitted on : Monday, April 11, 2016 - 4:29:37 PM
Last modification on : Thursday, February 7, 2019 - 4:02:30 PM

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  • HAL Id : hal-01301087, version 1

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Mazen Alsarem, Pierre-Edouard Portier, Sylvie Calabretto, Harald Kosch. SEMashup: Making Use of Linked Data for Generating Enhanced Snippets. AI Mashup Challenge 2014, 2014, pp.10. ⟨hal-01301087⟩

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