Enterprise Knowledge Management and Knowledge Platform Development in the Age of Large AI Models

The session, presented by Wang Chaolun, senior business manager of the Big Data and Intelligence Department at the China Academy of Information and Communications Technology, introduced the academy's role as an innovation think‑tank for the information society and its early work on knowledge graphs since 2018.

It outlined the department’s focus on providing comprehensive support to the Ministry of Industry and Information Technology and building platforms for industry innovation, emphasizing the importance of high‑quality knowledge accumulation for large‑model applications.

Key discussion points included an overview of the department’s work, challenges faced in enterprise knowledge capability building—such as hidden knowledge, inconsistent updates, and high technical thresholds—and the evolution of knowledge management from basic storage to knowledge engineering and finally to a knowledge‑centered middle‑platform era.

The presentation highlighted how large models and Retrieval‑Augmented Generation (RAG) can empower domain‑specific knowledge services, improve knowledge extraction, aggregation, and application, and create new business scenarios like digital employees and intelligent assistants.

Standardization efforts were described, including the development of the "Intelligent Knowledge Graph Technical Requirements" and "RAG Technical Requirements" standards, as well as a maturity model for enterprise knowledge platforms.

A Q&A segment clarified that the focus is on functional processes and scenario‑driven evaluation, and that while generic standards are a starting point, specific industry scenarios require tailored methodologies.

The session concluded with a summary of the discussed topics and an invitation for further collaboration on knowledge platform development.