The field of data analysis is witnessing a significant shift towards the integration of Large Language Models (LLMs) and Knowledge Graphs (KGs). This convergence enables the creation of dynamic, collaborative analytical ecosystems that can extract deep insights from complex, heterogeneous data. The use of LLMs and KGs is allowing researchers to overcome traditional limitations in data analysis, such as the static nature of KGs and the hallucination issues of LLMs. As a result, innovative methods are being developed for multi-dimensional data analysis, fault cause identification, and knowledge representation. Noteworthy papers in this area include:

• A method that utilizes LLM agents to extract product data from unstructured data and constructs a KG in real-time, supporting users in deep exploration and analysis of graph nodes.
• A framework that enhances FMEA reusability by combining manufacturing-domain conceptualization with graph neural network reasoning, achieving state-of-the-art performance in fault cause identification.
• A knowledge-driven framework that integrates standardized clinical terminology with a graph database to construct a structured medical knowledge graph, enabling multi-hop reasoning and ensuring terminological consistency.
• A novel knowledge modeling framework that elevates domains to first-class elements of conceptual representation, enabling context-aware reasoning and cross-domain analogy.
• A survey that provides a comprehensive overview of recent progress in LLM-empowered knowledge graph construction, outlining key trends and future research directions.