Optimised storage for datalog reasoning

Conference item

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.

Authors: Zhang, X; Hu, P; Nenov, Y; Horrocks, I

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for the Advancement of Artificial Intelligence
• Host title: Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024)
• Volume: 38
• Issue: 9
• Pages: 10748-10755
• Publication date: 2024-03-24
• Acceptance date: 2023-12-09
• Event title: 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024)
• Event location: Vancouver, Canada
• Event website: https://aaai.org/conference/aaai/aaai-24/
• Event start date: 2024-02-20
• Event end date: 2024-02-27
• DOI: 10.1609/aaai.v38i9.28947
• EISSN: 2374-3468
• ISSN: 2159-5399
• Language: English
• Keywords: semantic web; KRR; ontologies; DMKM; logic programming