Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions.

翻译：基于嵌入的知识图谱补全方法在过去几年中受到了广泛关注。当前大多数算法将知识图谱视为多向标记图，缺乏捕捉模式信息背后语义的能力。与此同时，大量信息已被捕获在大语言模型中，这为人工智能领域带来了革命性变革。知识图谱与大语言模型可实现相互增益。本文基于知识图谱嵌入生成方式的差异，对现有知识图谱补全算法展开探讨。首先论述了直推式和归纳式链接预测、实体类型预测等各类知识图谱补全算法，继而探讨了利用知识图谱内部类型信息、大语言模型以及捕捉不同描述逻辑公理语义的算法。最后，本文对当前研究现状进行批判性反思，并提出了未来研究方向建议。