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PyClause - Simple and efficient rule handling for knowledge graphs
Betz, Patrick
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Galarraga, Luis
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Ott, Simon
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Meilicke, Christian
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Suchanek, Fabian M.
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Stuckenschmidt, Heiner
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Document Type:
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Conference or workshop publication
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Year of publication:
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2024
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Book title:
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Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track : Jeju, 03-09 August 2024
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The title of a journal, publication series:
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tba
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Volume:
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tba
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Page range:
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1-3
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Conference title:
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IJCAI 2024, 33rd International Joint Conference on Artificial Intelligence
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Location of the conference venue:
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Jeju, South Korea
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Date of the conference:
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03.-09.08.2024
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Place of publication:
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Jeju, South Korea
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Publishing house:
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International Joint Conferences on Artificial Intelligence
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Subject:
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004 Computer science, internet
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Abstract:
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Rule mining finds patterns in structured data such as knowledge graphs. Rules can predict facts, help correct errors, and yield explainable insights about the data. However, existing rule mining implementations focus exclusively on mining rules -- and not on their application. The PyClause library offers a rich toolkit for the application of the mined rules: from explaining facts to predicting links, scoring rules, and deducing query results. The library is easy to use and can handle
substantial data loads.
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 Search Authors in
BASE:
Betz, Patrick
;
Galarraga, Luis
;
Ott, Simon
;
Meilicke, Christian
;
Suchanek, Fabian M.
;
Stuckenschmidt, Heiner
Google Scholar:
Betz, Patrick
;
Galarraga, Luis
;
Ott, Simon
;
Meilicke, Christian
;
Suchanek, Fabian M.
;
Stuckenschmidt, Heiner
ORCID:
Betz, Patrick ; Galarraga, Luis ; Ott, Simon ; Meilicke, Christian  ORCID: 0000-0002-0198-5396 ; Suchanek, Fabian M. ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859
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