论文标题
超图上的时间归纳逻辑推理
Temporal Inductive Logic Reasoning over Hypergraphs
论文作者
论文摘要
归纳逻辑推理是图形分析中的一项基本任务，该任务旨在从数据中概括模式。使用电感逻辑编程（ILP）等技术，已经针对传统图表（例如知识图（kg））对此任务进行了广泛的研究。现有的ILP方法假设从具有静态事实和二进制关系的KG学习。除KGS外，图形结构在其他应用程序中广泛存在，例如程序说明，场景图和程序执行。虽然ILP对这些应用是有益的，但将其应用于这些图是非平凡的：它们比通常涉及时间戳和N- ARY关系的kg更复杂，实际上是一种带有时间事件的超图。在这项工作中，我们提出了时间归纳逻辑推理（TILR），这是一种有关时间超图的ILP方法。为了启用HyperGraph推理，我们介绍了多启动的随机B-Walk，这是一种新型的超图形遍历方法。通过将其与路径矛盾算法相结合，TILR通过从时间和关系数据中概括来学习逻辑规则。为了解决缺乏超图基准测试，我们创建并发布了两个暂时的超graph数据集：YouCook2-HG和Nuscenes-HG。这些基准测试的实验表明，TILR比各种强基础具有较高的推理能力。
Inductive logic reasoning is a fundamental task in graph analysis, which aims to generalize patterns from data. This task has been extensively studied for traditional graph representations, such as knowledge graphs (KGs), using techniques like inductive logic programming (ILP). Existing ILP methods assume learning from KGs with static facts and binary relations. Beyond KGs, graph structures are widely present in other applications such as procedural instructions, scene graphs, and program executions. While ILP is beneficial for these applications, applying it to those graphs is nontrivial: they are more complex than KGs, which usually involve timestamps and n-ary relations, effectively a type of hypergraph with temporal events. In this work, we propose temporal inductive logic reasoning (TILR), an ILP method that reasons on temporal hypergraphs. To enable hypergraph reasoning, we introduce the multi-start random B-walk, a novel graph traversal method for hypergraphs. By combining it with a path-consistency algorithm, TILR learns logic rules by generalizing from both temporal and relational data. To address the lack of hypergraph benchmarks, we create and release two temporal hypergraph datasets: YouCook2-HG and nuScenes-HG. Experiments on these benchmarks demonstrate that TILR achieves superior reasoning capability over various strong baselines.
