arXiv cs.AI· Naixing Xu·· 3 天前AI 评分20
QORH：面向归纳式知识图谱链接预测的查询特定有序规则超图
QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction
AI 导读
QORH 将规则头和有序规则体作为以查询关系为条件的超边联合编码，显式建模多个关系间的联合依赖，并用规则置信度、覆盖率等统计证据进行可学习规则选择。模型通过角色感知超图消息传递增强关系表示，共享参数不依赖具体实体或关系标识，可对未见实体和关系做归纳预测。在实体归纳与完全归纳基准上，QORH 显著优于所评估的 SOTA 基线。
正文 · 原文
Abstract:Knowledge graph link prediction involving unseen entities and relations requires learning transferable reasoning patterns from observed graph structure. Existing methods based on relation graphs primarily capture pairwise dependencies, limiting their ability to preserve the compositional semantics, relation ordering, and role information of complete path rules. To address this limitation, we propose QORH (Query-specific Ordered Rule Hypergraph), which jointly encodes rule heads and ordered rule bodies as hyperedges conditioned on the query relation, explicitly modeling joint dependencies among multiple relations. QORH performs learnable rule selection using statistical evidence, such as rule confidence and coverage, and enhances relation representations through role-aware hypergraph message passing to guide entity-level reasoning. The model's shared parameters are independent of specific entity or relation identifiers, enabling inductive prediction for unseen entities and relations. Extensive experiments on entity-inductive and fully inductive benchmarks demonstrate that QORH significantly outperforms the state-of-the-art baselines included in our evaluation, validating the effectiveness of rule hypergraph modeling for inductive link prediction.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18198 [cs.AI] |
| (or arXiv:2610.18198v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18198 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Naixing Xu [view email]
[v1]
Tue, 13 Oct 2026 11:35:35 UTC (1,822 KB)
来源：arXiv cs.AI · arxiv.org