[2410.07708] Learning Tree Pattern Transformations
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Computer Science > Machine Learning

arXiv:2410.07708 (cs)
[Submitted on 10 Oct 2024 (v1), last revised 18 Feb 2025 (this version, v2)]

Title:Learning Tree Pattern Transformations

Authors:Daniel Neider, Leif Sabellek, Johannes Schmidt, Fabian Vehlken, Thomas Zeume
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Abstract:Explaining why and how a tree $t$ structurally differs from another tree $t^\star$ is a question that is encountered throughout computer science, including in understanding tree-structured data such as XML or JSON data. In this article, we explore how to learn explanations for structural differences between pairs of trees from sample data: suppose we are given a set $\{(t_1, t_1^\star),\dots, (t_n, t_n^\star)\}$ of pairs of labelled, ordered trees; is there a small set of rules that explains the structural differences between all pairs $(t_i, t_i^\star)$? This raises two research questions: (i) what is a good notion of "rule" in this context?; and (ii) how can sets of rules explaining a data set be learned algorithmically?
We explore these questions from the perspective of database theory by (1) introducing a pattern-based specification language for tree transformations; (2) exploring the computational complexity of variants of the above algorithmic problem, e.g. showing NP-hardness for very restricted variants; and (3) discussing how to solve the problem for data from CS education research using SAT solvers.
Comments: Full version of the ICDT 2025 paper
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Databases (cs.DB)
Cite as: arXiv:2410.07708 [cs.LG]
  (or arXiv:2410.07708v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2410.07708

Submission history

From: Fabian Vehlken [view email]
[v1] Thu, 10 Oct 2024 08:20:57 UTC (259 KB)
[v2] Tue, 18 Feb 2025 15:41:56 UTC (259 KB)
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