提出几何化多跳推理模型，让逻辑运算完全由空间变换实现
Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations
- 所有逻辑操作映射为纯几何变换，无需神经网络学习
- 引入传递性损失函数，确保三元组传递规则在所有情况下成立
- 兼具可解释性与竞争力，适合需要透明推理的场景
知识图谱上的多跳逻辑推理需要将逻辑语义准确映射到潜在空间。现有几何嵌入方法通过将实体映射到几何区域、逻辑操作映射到潜在变换，在该任务中表现良好。然而，当前方法仅利用实体的几何结构，未将逻辑操作完全映射为纯几何变换，仍依赖神经组件学习这些操作。相比之下，纯神经方法性能更优，但潜在空间缺乏可解释性。本文提出GeometrE，一种用于多跳推理的几何嵌入方法，将每个逻辑操作映射为潜在空间中的纯几何操作。此外，提出一种传递性损失函数，证明其能保持所有a,b,c满足：r(a,b) 且 r(b,c) → r(a,c) 的逻辑规则。实验表明，GeometrE优于现有最先进几何方法，并在标准基准数据集上与主流神经方法保持竞争性。
原文摘要 · Abstract (English)
Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space. Current geometric embedding methods show to be useful on this task by mapping entities to geometric regions and logical operations to latent transformations. While a geometric embedding can provide a direct interpretability framework for query answering, current methods have only leveraged the geometric construction of entities, failing to map logical operations to pure geometric transformations and, instead, using neural components to learn these operations. On the other hand, purely neural-based methods outperform geometric methods, but they lack interpretability in the latent space. We introduce GeometrE, a geometric embedding method for multi-hop reasoning, that maps every logical operation to a purely geometric operation in the latent space. Additionally, we introduce a transitive loss function and show that, unlike existing methods, it can preserve the logical rule for all a,b,c: r(a,b) and r(b,c) -> r(a,c). Our experiments show that GeometrE outperforms current state-of-the-art geometric methods and remains competitive with existing neural-based methods on standard benchmark datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。