Verifying Datalog Reasoning with Lean (Extended Abstract)

Johannes TantowJohannes Tantow, Lukas GerlachLukas Gerlach, Stephan MennickeStephan Mennicke, Markus KrötzschMarkus Krötzsch

Johannes Tantow, Lukas Gerlach, Stephan Mennicke, Markus Krötzsch
Verifying Datalog Reasoning with Lean (Extended Abstract)
KR 2025 - Recently Published Research Track, November 2025

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KurzfassungAbstract
Datalog is an essential logical rule language with many applications and modern rule engines compute logical consequences for Datalog with high performance and scalability. While Datalog is rather simple and, in principle, explainable by design, sophisticated implementations and optimizations are hard to verify. We therefore propose a certificate-based approach to validate results of Datalog reasoners in a formally verified checker for Datalog proofs. Using the proof assistant Lean, we implement such a checker and verify its correctness against direct formalizations of the Datalog semantics. We propose two JSON encodings for Datalog proofs: one using the widely supported Datalog proof trees and one using directed acyclic graphs for succinctness. To evaluate feasibility and performance of our approach, we validate proofs that we obtain by converting derivation traces of an existing Datalog reasoner into our tool-independent format.
Weitere Informationen unter:Further Information: Link
Projekt:Project: Cfaed, CPEC, CeTI, SECAI, ScaDS.AI
Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems

@inproceedings{TGMK2025,
  author    = {Johannes Tantow and Lukas Gerlach and Stephan Mennicke and Markus
               Kr{\"{o}}tzsch},
  title     = {Verifying Datalog Reasoning with Lean (Extended Abstract)},
  booktitle = {KR 2025 - Recently Published Research Track},
  year      = {2025},
  month     = {November}
}