PyClause - Simple and efficient rule handling for knowledge graphs

Betz, Patrick ; Galarraga, Luis ; Ott, Simon ; Meilicke, Christian ; Suchanek, Fabian M. ; Stuckenschmidt, Heiner

Document Type: Conference or workshop publication
Year of publication: 2024
Book title: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track : Jeju, 03-09 August 2024
The title of a journal, publication series: tba
Volume: tba
Page range: 1-3
Conference title: IJCAI 2024, 33rd International Joint Conference on Artificial Intelligence
Location of the conference venue: Jeju, South Korea
Date of the conference: 03.-09.08.2024
Place of publication: Jeju, South Korea
Publishing house: International Joint Conferences on Artificial Intelligence
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: 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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