Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact
This is an incremental survey paper that provides a historical overview and framework for understanding knowledge graph development, primarily for researchers and practitioners in AI and industry.
The paper categorizes three generations of knowledge graphs—entity-based, text-rich, and dual neural KGs integrating with LLMs—and describes their evolution and industry applications, but does not present new experimental results or concrete numbers.
Knowledge Graphs (KGs) have been used to support a wide range of applications, from web search to personal assistant. In this paper, we describe three generations of knowledge graphs: entity-based KGs, which have been supporting general search and question answering (e.g., at Google and Bing); text-rich KGs, which have been supporting search and recommendations for products, bio-informatics, etc. (e.g., at Amazon and Alibaba); and the emerging integration of KGs and LLMs, which we call dual neural KGs. We describe the characteristics of each generation of KGs, the crazy ideas behind the scenes in constructing such KGs, and the techniques developed over time to enable industry impact. In addition, we use KGs as examples to demonstrate a recipe to evolve research ideas from innovations to production practice, and then to the next level of innovations, to advance both science and business.