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Modern Datalog: Concepts, Methods, Applications (Invited Paper)

Author Markus Krötzsch

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ACM Subject Classification
  • Computing methodologies → Logic programming and answer set programming
  • Theory of computation → Constraint and logic programming
  • Theory of computation → Database query languages (principles)
  • Theory of computation → Logic and databases
Keywords
  • Datalog
  • query language
  • knowlegde representation and reasoning
  • logic programming
  • Horn logic
  • SPARQL
  • datatypes and aggregation
  • lecture notes
  • tutorial

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Abstract

Pure Datalog is arguably the most fundamental rule language, elegant and simple, but also often too limited to be useful in practice. This has motivated the introduction of many new expressive features, ranging from datatypes and related functions, over aggregates and semi-ring generalisations, to existential quantifiers and complex terms. In spite of their variety, all these approaches remain true to the nature of Datalog as a direct, pattern-based way of computing on structured data. We therefore find that a modern notion of Datalog is emerging, distinctly different from other approaches of logic programming and with its own set of related methods and applications.
In this course, we introduce Datalog and its most common extensions, and explain when and how these features can be used together (which is often, but not always, safe to do). We further look at modern Datalog systems and some of their primary use cases. Hands-on work with Datalog and its extensions is done with the free Datalog engine https://knowsys.github.io/nemo-doc/. The course is accessible to all audiences and does not assume specific prior knowledge.

Cite As Get BibTex

Markus Krötzsch. Modern Datalog: Concepts, Methods, Applications (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 7:1-7:41, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.RW.2024/2025.7

Author Details

Markus Krötzsch
  • Knowledge-Based Systems Group, TU Dresden, Germany

Funding

This work is partly supported by DFG in project 389792660 (TRR 248, https://www.perspicuous-computing.science/), by BMFTR in the https://www.scads.de/ (https://scads.ai/), and by BMFTR and DAAD (German Academic Exchange Service) in project 57616814 (https://www.secai.org/, https://www.secai.org/).

Acknowledgements

The practical hands-on exercises in these lecture notes rely on the Nemo rule engine. Thanks are due to all past and present contributors, but especially to Lukas Gerlach, Alex Ivliev, and Maximilian Marx for getting key features to work in time for this lecture. The proof tree visualisation in Figure 3 has been implemented by Tobias Wieland with contributions from Julián Méndez and Lukas Gerlach.

Supplementary Materials

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