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A tutorial on query answering and reasoning over probabilistic knowledge bases

Abstract:

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry alike. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. This tutorial is dedicated to give an understanding of various query answering and reasoning tasks that can be used to exploit the full potential of probabilistic knowledge bases. In the first part of the tutorial, we focus on (tuple-indepe...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
  • (Accepted manuscript, pdf, 531.1KB)
Publisher copy:
10.1007/978-3-030-00338-8_3

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Oxford college:
Hertford College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-7644-1668
Publisher:
Springer Verlag Publisher's website
Journal:
Reasoning Web. Learning, Uncertainty, Streaming, and Scalability 14th International Summer School 2018 Journal website
Volume:
11078
Pages:
35-77
Series:
Lecture Notes in Computer Science
Host title:
Reasoning Web. Learning, Uncertainty, Streaming, and Scalability 14th International Summer School 2018
Publication date:
2018-09-22
Acceptance date:
2018-07-28
Event location:
Esch-sur-Alzette, Luxembourg
DOI:
ISSN:
0302-9743
Source identifiers:
922425
ISBN:
9783030003371
Keywords:
Pubs id:
pubs:922425
UUID:
uuid:13cae303-2ea6-4c3c-8e28-570924116ea6
Local pid:
pubs:922425
Deposit date:
2018-10-27

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