Ecole d'ingénieur et centre de recherche en Sciences du numérique

SNARC - An approach for aggregating and recommending contextualized social content

Assaf, Ahmad; Senart, Aline; Troncy, Raphaël

ESWC 2013, 10th Extended Semantic Web Conference, Sesssion: AI Mashup Challenge, May 26-30, 2013, Montpellier, France / Also published in LNCS, Volume 7955/2013

Best AI Mashup Award

The Internet has created a paradigm shift in how we consume and disseminate information. Data nowadays is spread over heterogeneous silos of archived and live data. People willingly share data on social media by posting news, views, presentations, pictures and videos. SNARC is a service that uses semantic web technology and combines services available on the web to aggregate social news. SNARC brings live and archived information to the user that is directly related to his active page. The key advantage is an instantaneous access to complementary information without the need to dig for it. Information appears when it is relevant enabling the user to focus on what is really important.

Document Doi Bibtex

Titre:SNARC - An approach for aggregating and recommending contextualized social content
Type:Poster / Demo
Langue:English
Ville:Montpellier
Pays:FRANCE
Date:
Département:Data Science
Eurecom ref:4032
Copyright: © Springer. Personal use of this material is permitted. The definitive version of this paper was published in ESWC 2013, 10th Extended Semantic Web Conference, Sesssion: AI Mashup Challenge, May 26-30, 2013, Montpellier, France / Also published in LNCS, Volume 7955/2013 and is available at : http://dx.doi.org/10.1007/978-3-642-41242-4_58
Bibtex: @poster / demo{EURECOM+4032, year = {2013}, title = {{SNARC} - {A}n approach for aggregating and recommending contextualized social content}, author = {{A}ssaf, {A}hmad and {S}enart, {A}line and {T}roncy, {R}apha{\"e}l}, number = {EURECOM+4032}, month = {05}, institution = {Eurecom} address = {{M}ontpellier, {FRANCE}}, url = {http://www.eurecom.fr/publication/4032} }
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