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.

翻译：知识图谱（Knowledge Graphs，KGs）已被广泛应用于从网络搜索到个人助理的各类场景。本文阐述了三代知识图谱：基于实体的知识图谱，它们支撑着通用搜索与问答系统（如谷歌和必应）；富文本知识图谱，它们服务于产品搜索推荐、生物信息学等领域（如亚马逊和阿里巴巴）；以及新兴的知识图谱与大语言模型融合范式——我们称之为双神经知识图谱。我们分别描述了每代知识图谱的核心特征、构建过程中不为人知的疯狂创意，以及推动产业应用的关键技术演进。此外，我们将知识图谱作为范例，展示如何将研究从创新想法转化为生产实践，进而开启新一轮创新循环，实现科学与商业的共同进步。