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.
Keywords
- Data Mining: DM: Knowledge graphs and knowledge base completion
- Knowledge Representation and Reasoning: KRR: Learning and reasoning
- Knowledge Representation and Reasoning: KRR: Semantic Web
