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Query Languages for Machine-Learning Models (Invited Talk)

Author Martin Grohe

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LIPIcs.STACS.2026.1.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Database query languages (principles)
  • Theory of computation → Finite Model Theory
  • Theory of computation → Machine learning theory
Keywords
  • Expressive power of query languages
  • fixed-point logics
  • weighted structures
  • neural networks
  • explainable AI

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Abstract

In my invited talk and this accompanying paper, I discuss two logics for weighted finite structures: first-order logic with summation (FO(SUM)) and its recursive extension IFP(SUM). These logics originate from foundational work by Grädel, Gurevich, and Meer in the 1990s. In recent joint work with Standke, Steegmans, and Van den Bussche, we have investigated these logics as query languages for machine learning models, specifically neural networks, which are naturally represented as weighted graphs. I present illustrative examples of queries to neural networks that can be expressed in these logics and discuss fundamental results on their expressiveness and computational complexity.

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Martin Grohe. Query Languages for Machine-Learning Models (Invited Talk). In 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 364, pp. 1:1-1:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.STACS.2026.1

Author Details

Martin Grohe
  • RWTH Aachen University, Germany

Funding

Funded by the European Union (ERC, SymSim, 101054974). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Acknowledgements

Thanks to Nicole Schweikardt, Erich Grädel, and Jan Van den Bussche for valuable comments on an earlier version of this paper.

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