标题： 基于关系网络的归纳知识图谱补全逻辑推理 显示英文标题

标题： Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion

摘要： 归纳知识图谱补全（KGC）旨在推断一组新出现的实体的缺失关系，这些实体从未出现在训练集中。 这种设置更符合现实情况，因为现实世界的知识图谱（KG）不断演进并引入新的知识。 最近的研究表明，通过在子图上进行信息传递来嵌入新出现的实体，可以为归纳式KGC取得有前景的结果。 然而，这些方法的归纳能力通常受到两个关键问题的限制。 (i) KGC总是面临数据稀疏性问题，而在归纳式KGC中，新实体往往与原始KG几乎没有或没有连接，这种情况更加严重。 (ii) 冷启动问题。 通过从少量邻居收集局部信息来生成新实体的表示，对于精确的KG推理来说过于粗略。 为此，我们提出了一种新颖的归纳式知识图谱补全关系网络，即NORAN，用于归纳式KG补全。 它的目标是挖掘归纳式KG补全的潜在关系模式。 具体而言，以关系为中心，NORAN提供了一个对KG建模的超视图，在该视图中，关系之间的相关性可以自然地作为与实体无关的逻辑证据来进行归纳式KGC。 在五个基准测试上的大量实验结果表明，我们的框架显著优于最先进的KGC方法。 显示英文摘要

摘要： Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are constantly evolving and introducing new knowledge. Recent studies have shown promising results using message passing over subgraphs to embed newly-coming entities for inductive KGC. However, the inductive capability of these methods is usually limited by two key issues. (i) KGC always suffers from data sparsity, and the situation is even exacerbated in inductive KGC where new entities often have few or no connections to the original KG. (ii) Cold-start problem. It is over coarse-grained for accurate KG reasoning to generate representations for new entities by gathering the local information from few neighbors. To this end, we propose a novel iNfOmax RelAtion Network, namely NORAN, for inductive KG completion. It aims to mine latent relation patterns for inductive KG completion. Specifically, by centering on relations, NORAN provides a hyper view towards KG modeling, where the correlations between relations can be naturally captured as entity-independent logical evidence to conduct inductive KGC. Extensive experiment results on five benchmarks show that our framework substantially outperforms the state-of-the-art KGC methods.