Enterprise Knowledge Brain Powered by Large Models and Knowledge Graphs

The presentation introduces the topic "Enterprise Knowledge Brain Driven by Large Models" and outlines four main sections: challenges and opportunities of knowledge management, the architecture of the enterprise knowledge brain, key technologies, and application cases.

1. Knowledge Management Challenges and Opportunities – With data volume projected to explode by 2025 and unstructured data dominating, enterprises face difficulties in data integration, permission control, storage efficiency, and extracting actionable insights. Large models and knowledge graphs offer powerful tools to address these issues.

2. Enterprise Knowledge Brain Architecture – The brain consists of a model platform (supporting models such as DeepSeek, proprietary multimodal models, OCR, translation, etc.), a knowledge‑graph platform (providing knowledge construction, completion, management, and quality assurance), an application platform (knowledge base, intelligent analytics, dialogue, and assistant services), and a business‑scenario layer. Core data flow transforms multimodal inputs into a high‑quality graph that powers multimodal search, visualization, and intelligent Q&A.

3. Key Technologies – • Intelligent Graph Modeling : Pre‑processing, document splitting, schema extraction, fusion, and completion driven by large models reduce manual effort and improve scalability. • Model Acceleration & Multi‑Instance Deployment : Enhances processing capacity for large‑scale data. • Large‑Model‑Driven Knowledge Extraction : Automates extraction, fusion, and enrichment of graph data. • Graph‑Based Question Answering & Reasoning : Combines graph reasoning with large‑model inference to deliver accurate, multi‑step answers. • Graph Query Generation : Translates natural language into graph query language (e.g., Cypher/SQL) with calibration steps. • GraphRAG : Integrates graph structures with retrieval‑augmented generation for comprehensive Q&A. • Application Platform Features : Visual workflow orchestration and dynamic chatbot configuration for diverse enterprise scenarios.

4. Application Cases – Demonstrates how the knowledge brain overcomes the limitations of traditional knowledge bases by providing global knowledge insight, traceability, and reasoning capabilities, thereby supporting intelligent decision‑making in policy analysis, public security, healthcare, and more.

In conclusion, the integration of large models and knowledge graphs forms a robust, scalable foundation for modern enterprise knowledge management, enabling efficient data handling, intelligent retrieval, and advanced reasoning across various business domains.