PyClause - Simple and Efficient Rule Handling for Knowledge Graphs

Patrick Betz; Luis Galárraga; Simon Ott; Christian Meilicke; Fabian Suchanek; Heiner Stuckenschmidt

IJCAI 2024poster2 citations

PyClause - Simple and Efficient Rule Handling for Knowledge Graphs

Patrick Betz, Luis Galárraga, Simon Ott, Christian Meilicke, Fabian Suchanek, Heiner Stuckenschmidt

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.

Data Mining: DM: Knowledge graphs and knowledge base completionKnowledge Representation and Reasoning: KRR: Learning and reasoningKnowledge Representation and Reasoning: KRR: Semantic Web

BibTeX

@inproceedings{ijcai2024p991,
  title     = {PyClause - Simple and Efficient Rule Handling for Knowledge Graphs},
  author    = {Betz, Patrick and Galárraga, Luis and Ott, Simon and Meilicke, Christian and Suchanek, Fabian and Stuckenschmidt, Heiner},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {8610--8613},
  year      = {2024},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2024/991},
  url       = {https://doi.org/10.24963/ijcai.2024/991},
}