用大模型打通企业数据孤岛，构建智能知识图谱。
LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics
- 用大模型自动提取实体、推断关系、语义增强，整合多源数据。
- 在专家发现、任务管理、决策支持上显著提升效率。
- 适合需要跨系统智能分析的企业用户，如产品与客服团队。
企业内部数据孤岛阻碍了可行动洞察的提取，影响产品开发、客户互动、会议准备及数据分析决策的效率。本文提出一种框架，利用大语言模型（LLMs）将多种数据源统一为以活动为中心的综合性知识图谱。该框架自动化完成实体抽取、关系推理和语义增强，支持对邮件、日历、聊天、文档、日志等多类型数据的高级查询、推理与分析。系统具备高灵活性，可应用于上下文搜索、任务优先级排序、专家发现、个性化推荐及趋势识别等场景。实验表明，在专家发现、任务管理与数据驱动决策方面表现优异。通过融合大模型与知识图谱，该方案连接了分散系统，赋能智能化企业分析工具。
原文摘要 · Abstract (English)
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.
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