Knowledge Graphs (KGs) are foundational structures in many AI applications,
representing entities and their interrelations through triples. However,
triple-based KGs lack the contextual information of relational knowledge, like
temporal dynamics and provenance details, which are crucial for comprehensive
knowledge representation and effective reasoning. Instead, \textbf{Contextual
Knowledge Graphs} (CKGs) expand upon the conventional structure by
incorporating additional information such as time validity, geographic
location, and source provenance. This integration provides a more nuanced and
accurate understanding of knowledge, enabling KGs to offer richer insights and
support more sophisticated reasoning processes. In this work, we first discuss
the inherent limitations of triple-based KGs and introduce the concept of
contextual KGs, highlighting their advantages in knowledge representation and
reasoning. We then present \textbf{KGR$^3$, a context-enriched KG reasoning
paradigm} that leverages large language models (LLMs) to retrieve candidate
entities and related contexts, rank them based on the retrieved information,
and reason whether sufficient information has been obtained to answer a query.
Our experimental results demonstrate that KGR$^3$ significantly improves
performance on KG completion (KGC) and KG question answering (KGQA) tasks,
validating the effectiveness of incorporating contextual information on KG
representation and reasoning.

本文介绍了知识图谱（KGs）以及其与关系知识的上下文信息的整合，重点讨论了基于三元组的 KGs 存在的局限性和上下文 KGs 的优势，并提出了 KGR$^3$，一个利用大型语言模型（LLMs）进行 KG 推理的范例，实验证明 KGR$^3$ 显著提高了 KG 补全和 KG 问答任务的性能，验证了将上下文信息整合到 KG 表示和推理中的有效性。