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What and Who
Title:What knowledge bases know (and what they don't)
Speaker:Simon Razniewski
coming from:Max-Planck-Institut für Informatik - D5
Speakers Bio:
Event Type:Joint Lecture Series
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 4 October 2017
Duration:60 Minutes
Building:E1 5
Knowledge bases such as Wikidata, YAGO, or the Google Knowledge Graph are increasingly used in applications like entity resolution, structured search or question answering. The correctness of these knowledge bases is generally well understood and at the center of attention in automated construction techniques. About their completeness, in contrast, most knowledge is anecdotal: Completeness is usually correlated with popularity of topics, yet understanding whether a knowledge base has complete information on a specific topic is often difficult.

In this talk I will introduce the problem of understanding the completeness of knowledge bases. I will present three approaches: (i) data-inherent inferences about completeness using association rule mining, (ii) extraction of cardinality information from text sources, and (iii) various tools we developed to help knowledge base curators and consumers in mapping and understanding the completeness of knowledge bases.
Name(s):Jennifer Müller
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):
Jennifer Müller/MPI-INF, 08/24/2017 12:24 PM
Last modified:
Uwe Brahm/MPII/DE, 10/04/2017 07:01 AM
  • Jennifer Müller, 08/24/2017 12:26 PM -- Created document.