为知识图谱中的不确定性提供可扩展的推理框架
Scalable Uncertainty Reasoning in Knowledge Graphs
- 分三层次设计专用推理方法：概率属性、不确定三元组、模式缺失
- 将SPARQL证明转换为可计算的概率电路，支持高效查询
- 适合需要高精度与高效推理的知识图谱应用者
知识图谱在语义数据集成中至关重要，但其建模的真实世界数据往往具有内在不确定性。这种不确定性体现在三个层面：属性值不精确、三元组存在性概率化以及模式知识不完整。然而，当前语义网标准缺乏对不确定性的原生支持，简单扩展常导致计算不可行。本文提出一个模块化框架，针对每个层面采用定制技术：(1) 定义概率谓词与连续属性的查询代数；(2) 基于编译的框架，将SPARQL证明转化为可处理的概率电路以应对不确定三元组；(3) 利用拓扑感知的几何嵌入实现统计模式推理。核心假设是，通过代数、逻辑与几何等专门化推理机制，可在保持语义精确性的同时实现计算可处理性。
原文摘要 · Abstract (English)
Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncertainty manifests in three distinct levels: imprecise attribute values, probabilistic triple existence, and incomplete schema knowledge. However, current Semantic Web standards lack native support for reasoning over such uncertainty, and naïve extensions often incur computational intractability. In this thesis, I aim to develop a modular framework that addresses each level through tailored techniques: (1) defining probabilistic literals and a corresponding query algebra for continuous attributes; (2) a compilation-based framework transforming SPARQL provenance into tractable probabilistic circuits for uncertain triples; and (3) topology-aware geometric embeddings for statistical schema reasoning. The central hypothesis is that specialized reasoning mechanisms, namely algebraic, logical, and geometric approaches, can reconcile semantic precision with computational tractability.
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