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arXiv cs.AI· Bente Gortworst, Cem Okulmus, Magdalena Ortiz, Anni-Yasmin Turhan·· 12 小时前AI 评分18

从样例中学习形状:递归 SHACL 中形状学习的理论基础

Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

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研究从正负样例节点集合 P 和 N 出发,计算 SHACL 形状表达式 C 使其验证 P 中全部节点且不验证 N 中任何节点,C 采用对应 Description Logic ELI* 的 SHACL 核心片段。针对 well-founded、stable 和 supported 三种语义,论文解决了拟合存在性与最具体拟合计算问题,给出紧的指数时间上界,并在相关特例上得到多项式界。

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Abstract:SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known fitting approach to this task: given sets P and N of positive and negative example nodes from an input graph, compute a shape expression C, possibly using shape names defined in a recursive shape catalogue, that validates at every node in P and none in N. We focus on the case where C is written in a core fragment of SHACL corresponding to the Description Logic ELI*. For the catalogue, we consider the well-founded, stable, and supported semantics. We address fitting existence and most specific fitting computation, establish tight exponential-time upper bounds for both problems, and obtain polynomial bounds for relevant special cases.
Comments: full version of a paper accepted at ISWC26
Subjects: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
Cite as: arXiv:2607.27934 [cs.AI]
  (or arXiv:2607.27934v3 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2607.27934

arXiv-issued DOI via DataCite

Submission history

From: Cem Okulmus [view email]
[v1] Thu, 30 Jul 2026 09:45:22 UTC (89 KB)
[v2] Thu, 6 Aug 2026 12:02:36 UTC (88 KB)
[v3] Fri, 23 Oct 2026 13:52:59 UTC (88 KB)

来源:arXiv cs.AI · arxiv.org