Reasoning about the relationships between entities from input facts (e.g., whether Ari is a grandparent of Charlie) generally requires explicit consideration of other entities that are not mentioned in the query (e.g., the parents of Charlie). In this paper, we present an approach for learning to solve problems of this kind in large, real-world domains, using sparse and local hypergraph neural networks (SpaLoc). SpaLoc is motivated by two observations from traditional logic-based reasoning: relational inferences usually apply locally (i.e., involve only a small number of individuals), and relations are usually sparse (i.e., only hold for a small percentage of tuples in a domain). We exploit these properties to make learning and inference efficient in very large domains by (1) using a sparse tensor representation for hypergraph neural networks, (2) applying a sparsification loss during training to encourage sparse representations, and (3) subsampling based on a novel information sufficiency-based sampling process during training. SpaLoc achieves state-of-the-art performance on several real-world, large-scale knowledge graph reasoning benchmarks, and is the first framework for applying hypergraph neural networks on real-world knowledge graphs with more than 10k nodes.

翻译：从输入事实中推理实体间的关系（例如，判断阿里是否查理的祖父母）通常需要显式考虑查询中未提及的其他实体（如查理的父母）。本文提出一种方法，用于在大型真实世界领域中以稀疏和局部超图神经网络（SpaLoc）学习解决此类问题。SpaLoc 的提出基于传统逻辑推理的两点观察：关系推理通常局部适用（即仅涉及少量个体），且关系通常稀疏（即仅适用于领域中一小部分元组）。我们利用这些特性，通过以下方式实现超大规模领域中的高效学习与推理：（1）使用稀疏张量表示超图神经网络；（2）在训练过程中引入稀疏化损失以鼓励稀疏表示；（3）基于一种新颖的信息充分性采样过程进行子采样。SpaLoc 在多个真实世界大规模知识图谱推理基准上取得了最先进性能，且是首个将超图神经网络应用于超过 1 万个节点的真实知识图谱的框架。