In recent years, Natural Language Processing (NLP) has played a significant role in various Artificial Intelligence (AI) applications such as chatbots, text generation, and language translation. The emergence of large language models (LLMs) has greatly improved the performance of these applications, showing astonishing results in language understanding and generation. However, they still show some disadvantages, such as hallucinations and lack of domain-specific knowledge, that affect their performance in real-world tasks. These issues can be effectively mitigated by incorporating knowledge graphs (KGs), which organise information in structured formats that capture relationships between entities in a versatile and interpretable fashion. Likewise, the construction and validation of KGs present challenges that LLMs can help resolve. The complementary relationship between LLMs and KGs has led to a trend that combines these technologies to achieve trustworthy results. This work collected 28 papers outlining methods for KG-powered LLMs, LLM-based KGs, and LLM-KG hybrid approaches. We systematically analysed and compared these approaches to provide a comprehensive overview highlighting key trends, innovative techniques, and common challenges. This synthesis will benefit researchers new to the field and those seeking to deepen their understanding of how KGs and LLMs can be effectively combined to enhance AI applications capabilities.

翻译：近年来，自然语言处理（NLP）在聊天机器人、文本生成和语言翻译等各种人工智能（AI）应用中发挥了重要作用。大语言模型（LLMs）的出现极大地提升了这些应用的性能，在语言理解和生成方面展现出令人惊叹的结果。然而，它们仍存在一些缺陷，例如幻觉和缺乏领域特定知识，这影响了其在现实任务中的表现。通过融入知识图谱（KGs）可以有效缓解这些问题，知识图谱以结构化格式组织信息，以灵活且可解释的方式捕捉实体间的关系。同样，知识图谱的构建与验证也面临着挑战，而大语言模型有助于解决这些挑战。大语言模型与知识图谱之间的互补关系催生了一种融合这两种技术以获得可信结果的趋势。本研究收集了28篇论文，概述了知识图谱增强的大语言模型、基于大语言模型的知识图谱以及大语言模型-知识图谱混合方法。我们系统性地分析和比较了这些方法，提供了一个全面的综述，重点突出了关键趋势、创新技术和共同挑战。这一综合论述将有益于该领域的新研究人员，以及那些希望深入理解如何有效结合知识图谱与大语言模型以增强人工智能应用能力的研究者。