Integrating Knowledge Graphs with DeepSeek AI for Enterprise Knowledge Management

The talk introduces a novel approach that fuses knowledge graphs with DeepSeek intelligent agents to transform enterprise knowledge management. Knowledge graphs provide structured, factual, and explainable data, while DeepSeek offers powerful learning and reasoning capabilities, together creating a flexible, efficient, and decision‑supportive system.

Four main topics are covered: (1) DeepSeek’s technical highlights and limitations, including its open‑source R1 model, MoE architecture, low‑cost inference, and upcoming V3/R1 releases; (2) the "graph‑model complementarity" paradigm, which draws an analogy to symbiotic relationships in nature, explaining how structured knowledge graphs and unstructured large models can mutually enhance each other’s strengths.

The presentation details the distinct advantages of each component: knowledge graphs excel at factual accuracy, controllability, and interpretability, while large models excel at semantic understanding and multi‑task processing. Their combination enables reliable, explainable AI applications across domains such as finance, healthcare, and customer service.

A taxonomy of enterprise knowledge is described—Know‑What, Know‑Why, Know‑How, and Know‑Who—along with their characteristics and typical use‑cases, emphasizing the need for systematic organization and retrieval.

The speaker outlines a comprehensive framework where the large model acts as the brain and the knowledge graph serves as auxiliary networks, supporting use‑cases like knowledge search, aggregation, inheritance, Q&A, and proactive push.

Practical applications and future outlooks are presented, including case studies in medical diagnosis, financial risk control, and intelligent manufacturing, as well as a roadmap for multimodal knowledge bases, bidirectional traceability, and AI‑driven decision making.

Finally, the talk predicts that by 2027 more than 60% of enterprises will embed AI literacy into their data strategies, and the synergy of knowledge graphs and large models will become a cornerstone for the next wave of intelligent, trustworthy AI systems.