A Survey of Link Prediction in N-ary Knowledge Graphs

TL;DR

This paper provides the first comprehensive survey of link prediction methods in N-ary Knowledge Graphs, highlighting their categorization, performance, and future research directions.

Contribution

It systematically reviews existing link prediction techniques in NKGs, offering a structured overview and analysis of their applications and effectiveness.

Findings

Categorization of existing methods

Performance analysis of techniques

Future research directions outlined

Abstract

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.