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	    Raweb 
	    2017</a> | <a href="http://www.inria.fr/en/teams/cedar">Presentation of the Team CEDAR</a> | <a href="https://team.inria.fr/cedar/">CEDAR Web Site
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        <h2>Section: 
      New Results</h2>
        <h3 class="titre3">Representative Semantic Query Answers</h3>
        <p>The availability of large knowledge bases such as Yago or DBPedia allows theoretically anybody to tap in their resources through structured and semantics queries. Thi is still not as widespread as it could be, and we postulate this is mainly for two reasons. First, it is complex to write queries in such a setting. Second, the value added of such querying is improvable. We focused on the second point, with the rationale that increasing the value aded may motivate more easily users to spend the time and energy necessary to learn to write SPARQL queries. More specifically, the internship of M. Buron <a href="./bibliography.html#cedar-2017-bid1">[22]</a> explored the possibility of exploiting the reasoning performed to find a tuple as an answer to cluster answers in a semantic (and explainable) way.
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