Disconnected data silos within enterprises obstruct the extraction of actionable insights, diminishing efficiency in areas such as product development, client engagement, meeting preparation, and analytics-driven decision-making. This paper introduces a framework that uses large language models (LLMs) to unify various data sources into a comprehensive, activity-centric knowledge graph. The framework automates tasks such as entity extraction, relationship inference, and semantic enrichment, enabling advanced querying, reasoning, and analytics across data types like emails, calendars, chats, documents, and logs. Designed for enterprise flexibility, it supports applications such as contextual search, task prioritization, expertise discovery, personalized recommendations, and advanced analytics to identify trends and actionable insights. Experimental results demonstrate its success in the discovery of expertise, task management, and data-driven decision making. By integrating LLMs with knowledge graphs, this solution bridges disconnected systems and delivers intelligent analytics-powered enterprise tools.

本研究解决了企业内部数据孤岛问题，提出了一个利用大型语言模型（LLMs）将不同数据源统一为活动中心的知识图谱的框架。该框架通过自动化实体提取、关系推断和语义丰富化，提供了高级查询、推理和分析功能，成功提高了任务管理和基于数据的决策能力。

基于大型语言模型的企业智能与分析知识图谱