首次系统综述三元组以上知识图谱的链接预测方法
A Survey of Link Prediction in N-ary Knowledge Graphs
- 按建模方式分类现有链接预测方法
- 梳理不同方法在真实数据集上的表现差异
- 适合对复杂知识表示感兴趣的研习者
N-ary Knowledge Graphs（NKGs）是一种专门用于高效表达复杂现实世界事实的知识图谱。与传统知识图谱中通常仅涉及两个实体的事实不同，NKGs 可以捕捉包含超过两个实体的高阶事实。在 NKGs 中进行链接预测的目标是补全这些高阶事实中的缺失元素，这对完善 NKGs 及提升下游应用性能至关重要。该任务近年来受到广泛关注。本文首次全面综述了 NKGs 中的链接预测研究，系统梳理了该领域的进展，对现有方法进行了分类，并分析其性能与适用场景，同时指出了未来有潜力的研究方向。
原文摘要 · Abstract (English)
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significant attention. In this paper, we present the first comprehensive survey of link prediction in NKGs, providing an overview of the field, systematically categorizing existing methods, and analyzing their performance and application scenarios. We also outline promising directions for future research.
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