The Role of Knowledge Graphs in Industry: Importance, Product Forms, and Practical Cases

Introduction

This article shares the significance of knowledge graphs for the industry, outlines the mainstream product forms, and demonstrates their application through real cases.

Three Main Parts

Background introduction

Graph product forms

Industrial graph advancement

Background

Yunwen Technology started with chatbots (2013‑2019) and shifted to knowledge‑related fields after the release of BERT, recognizing that high‑quality enterprise knowledge is essential for improving QA systems. From 2020 to 2023 the company deepened its focus on knowledge, especially knowledge graphs.

In 2023, despite the hype around large models, many enterprises still value knowledge graphs for data governance and Retrieval‑Augmented Generation (RAG). High‑quality, structured knowledge remains a prerequisite for effective private LLMs.

Yunwen Technology emphasizes that knowledge graphs should complement, not replace, large models, and that building them requires close collaboration with business experts.

Graph Product Forms

The AI foundation should leverage third‑party LLM APIs/SDKs rather than reinventing the wheel. AI capability components (e.g., specialized modules) are often easier to sell than whole products.

Three application directions are explored: AIGC, knowledge intelligence, and intelligent services. Knowledge graphs are a core part of knowledge intelligence but not the only component.

Building a graph involves integrating relational databases, unstructured documents, and possibly graph networks, enabling multi‑source heterogeneous analysis for tasks such as policy compliance or fault diagnosis.

Graph storage (graph databases like Neo4j, Genius Graph, etc.) is critical because LLMs cannot yet process entire graphs or long texts efficiently.

Industrial Graph Advancement

In digital transformation, AI and graphs are used in scheduling, device management, marketing, and analytics. For example, a fire‑response schedule can be improved by modeling fire‑related entities, precautions, and cases in a graph.

Agents are important for multi‑task scheduling, and graph‑based knowledge can provide more precise, richer results than keyword search.

Device lifecycle management benefits from a graph that links factory data, maintenance records, and operational status across disparate systems.

When constructing graphs, design should be demand‑driven; over‑ambitious schemas that try to model everything become unwieldy and less useful.

Small‑but‑focused graphs (e.g., linking device basics, faults, and work orders) often deliver higher value than massive, generic graphs.

Conclusion

Effective industrial knowledge graphs require scenario‑driven design, close business‑technical collaboration, and integration with large models to achieve practical, high‑impact applications.