{"type":"ScholarlyArticle","title":"PyClause - Simple and Efficient Rule Handling for Knowledge Graphs","authors":[{"name":"Patrick Betz","slug":"patrick-betz","given":"Patrick","family":"Betz","affiliation":"University of Mannheim"},{"name":"Luis Galárraga","slug":"luis-galarraga","given":"Luis","family":"Galárraga","affiliation":"INRIA"},{"name":"Simon Ott","slug":"simon-ott","given":"Simon","family":"Ott","affiliation":"Austrian Institute of Technology GmbH"},{"name":"Christian Meilicke","slug":"christian-meilicke","given":"Christian","family":"Meilicke","affiliation":"University of Mannheim"},{"name":"Fabian Suchanek","slug":"fabian-suchanek","given":"Fabian","family":"Suchanek","affiliation":"Telecom Paris , Institut Polytechnique de Paris {patrick ,"},{"name":"Heiner Stuckenschmidt","slug":"heiner-stuckenschmidt","given":"Heiner","family":"Stuckenschmidt","affiliation":"University of Mannheim"}],"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.","doi":"10.24963/ijcai.2024/991","url":"https://ijcai.org/proceedings/2024/991","pdf":"https://ijcai.org/proceedings/2024/0991.pdf","markdown":"https://ijcai.org/proceedings/2024/991.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/991","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":991,"pages":{"first":8610,"last":8613},"keywords":["Knowledge Representation and Reasoning: KRR: Semantic Web","Knowledge Representation and Reasoning: KRR: Learning and reasoning","Data Mining: DM: Knowledge graphs and knowledge base completion"],"videos":[],"references":[{"position":1,"raw":"[Betz et al., 2022a] Patrick Betz, Christian Meilicke, and Heiner Stuckenschmidt. 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