A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects

TL;DR

This survey comprehensively reviews task-oriented knowledge graph reasoning, categorizing approaches, applications, and challenges, and discusses the impact of large language models on the field.

Contribution

It provides a systematic categorization of KGR tasks, applications, and challenging paradigms, including recent advances like large language models, which was lacking in previous surveys.

Findings

Categorizes KGR into static, dynamic, multi-modal, few-shot, and inductive tasks.

Highlights the role of large language models in advancing KGR.

Outlines future research directions in KGR.

Abstract

Knowledge graphs (KGs) have emerged as a powerful paradigm for structuring and leveraging diverse real-world knowledge, which serve as a fundamental technology for enabling cognitive intelligence systems with advanced understanding and reasoning capabilities. Knowledge graph reasoning (KGR) aims to infer new knowledge based on existing facts in KGs, playing a crucial role in applications such as public security intelligence, intelligent healthcare, and financial risk assessment. From a task-centric perspective, existing KGR approaches can be broadly classified into static single-step KGR, static multi-step KGR, dynamic KGR, multi-modal KGR, few-shot KGR, and inductive KGR. While existing surveys have covered these six types of KGR tasks, a comprehensive review that systematically summarizes all KGR tasks particularly including downstream applications and more challenging reasoning paradigms remains lacking. In contrast to previous works, this survey provides a more comprehensive perspective on the research of KGR by categorizing approaches based on primary reasoning tasks, downstream application tasks, and potential challenging reasoning tasks. Besides, we explore advanced techniques, such as large language models (LLMs), and their impact on KGR. This work aims to highlight key research trends and outline promising future directions in the field of KGR.