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What and Who
Title:Effective Searching of RDF Knowledge Graphs
Speaker:Hiba Arnaout
coming from:American University of Beirut -Lebanon
Speakers Bio:MSc in Computer Science - American University of Beirut -Lebanon
Event Type:PhD Application Talk
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Tuesday, 10 October 2017
Duration:60 Minutes
Building:E1 4
RDF knowledge graphs are typically searched using triple-pattern queries. Often, triple-pattern queries will return too many or too few results, making it difficult for users to find relevant answers to their information needs. To remedy this, we propose a general framework for effective searching of RDF knowledge graphs. Our framework extends both the searched knowledge graph and triple-pattern queries with keywords to allow users to form a wider range of queries. In addition, it provides result ranking based on statistical machine translation, and performs automatic query relaxation to improve query recall. Finally, we also define a notion of result diversity in the setting of RDF data and provide mechanisms to diversify RDF search results using Maximal Marginal Relevance. We evaluate the effectiveness of our retrieval framework using various carefully-designed user studies on DBpedia, a large and real-world RDF knowledge graph.
Name(s):IMPRS Office Team
Phone:0681 93251800
EMail:--email address not disclosed on the web
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Created by:Aaron Alsancak/MPI-INF, 10/09/2017 01:32 PMLast modified by:Uwe Brahm/MPII/DE, 10/10/2017 07:01 AM
  • Aaron Alsancak, 10/09/2017 01:46 PM
  • Aaron Alsancak, 10/09/2017 01:40 PM -- Created document.