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.
Type de document :
Article dans une revue
AI Mashup Challenge 2014, 2014, pp.10
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01301087
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : lundi 11 avril 2016 - 16:29:37
Dernière modification le : mercredi 31 octobre 2018 - 12:24:23

Identifiants

  • HAL Id : hal-01301087, version 1

Collections

Citation

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〉

Partager

Métriques

Consultations de la notice

194