Knowledge Representation Learning (KRL) is crucial for enabling applications of symbolic knowledge from Knowledge Graphs (KGs) to downstream tasks by projecting knowledge facts into vector spaces. Despite their effectiveness in modeling KG structural information, KRL methods are suffering from the sparseness of KGs. The rise of Large Language Models (LLMs) built on the Transformer architecture presents promising opportunities for enhancing KRL by incorporating textual information to address information sparsity in KGs. LLM-enhanced KRL methods, including three key approaches, encoder-based methods that leverage detailed contextual information, encoder-decoder-based methods that utilize a unified Seq2Seq model for comprehensive encoding and decoding, and decoder-based methods that utilize extensive knowledge from large corpora, have significantly advanced the effectiveness and generalization of KRL in addressing a wide range of downstream tasks. This work provides a broad overview of downstream tasks while simultaneously identifying emerging research directions in these evolving domains.

翻译：知识表示学习（KRL）通过将知识图谱（KG）中的符号知识事实投影到向量空间，对于实现知识图谱在下游任务中的应用至关重要。尽管KRL方法在建模知识图谱结构信息方面表现有效，但其仍受限于知识图谱的稀疏性。基于Transformer架构构建的大语言模型（LLM）的兴起，为通过融入文本信息以解决知识图谱信息稀疏性、从而增强KRL提供了广阔前景。LLM增强的KRL方法，主要包括三种关键范式：基于编码器的方法（利用详细的上下文信息）、基于编码器-解码器的方法（采用统一的Seq2Seq模型进行全面的编码和解码）以及基于解码器的方法（利用大规模语料库中的广泛知识），显著提升了KRL在处理广泛下游任务时的效能与泛化能力。本文对相关下游任务进行了广泛概述，同时指出了这些不断发展的领域中新兴的研究方向。