TY - GEN
T1 - PyClause - Simple and Efficient Rule Handling for Knowledge Graphs
AU - Betz, Patrick
AU - Galárraga, Luis
AU - Ott, Simon
AU - Meilicke, Christian
AU - Suchanek, Fabian
AU - Stuckenschmidt, Heiner
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85204301539
M3 - Conference contribution
AN - SCOPUS:85204301539
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 8610
EP - 8613
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -