Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact
Xin Luna Dong
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.