KR2022Proceedings of the 19th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning

Haifa, Israel. July 31–August 5, 2022.

Edited by
  1. Gabriele Kern-Isberner
  2. Gerhard Lakemeyer
  3. Thomas Meyer

ISSN: 2334-1033
ISBN: 978-1-956792-01-0

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Published by

Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization

Revisiting Semiring Provenance for Datalog

  1. Camille Bourgaux(DI ENS, ENS, CNRS, PSL University & Inria, Paris, France)
  2. Pierre Bourhis(CRIStAL, CNRS, University of Lille, Inria, Lille, France)
  3. Liat Peterfreund(LIGM, CNRS, Université Gustave Eiffel, ENPC, Paris, France)
  4. Michaël Thomazo(DI ENS, ENS, CNRS, PSL University & Inria, Paris, France)
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BibTeX
https://doi.org/10.24963/kr.2022/10

Keywords

  1. Ontology-based data access, integration, and exchange

Abstract

Data provenance consists in bookkeeping meta information during query evaluation, in order to enrich query results with their trust level, likelihood, evaluation cost, and more. The framework of semiring provenance abstracts from the specific kind of meta information that annotates the data. While the definition of semiring provenance is uncontroversial for unions of conjunctive queries, the picture is less clear for Datalog. Indeed, the original definition might include infinite computations, and is not consistent with other proposals for Datalog semantics over annotated data.

In this work, we propose and investigate several provenance semantics, based on different approaches for defining classical Datalog semantics. We study the relationship between these semantics, and introduce properties that allow us to analyze and compare them.