Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.

翻译：商业知识图谱对当今众多企业至关重要，其提供的事实知识与结构化数据能够赋能产品并提升智能化水平。尽管具有显著优势，商业知识图谱的构建仍需解决结构缺失与多模态数据等棘手问题。本文深入探讨了在复杂真实系统中构建知识图谱所面临的实践挑战，并介绍了源于阿里巴巴集团的开源商业知识图谱OpenBG的构建过程。具体而言，我们定义了涵盖各类抽象产品与消费需求的核心本体，并在部署应用中整合了细粒度分类体系与多模态事实。OpenBG是规模空前的开源商业知识图谱：包含26亿条三元组、8800余万实体，覆盖超过100万核心类/概念及2681种关系类型。我们向社区开放了由此衍生的全部资源（OpenBG基准测试集），并报告了以知识图谱为中心任务的实验结果。基于OpenBG基准测试，我们举办了在线竞赛，吸引了数千支参赛队伍。进一步，我们对OpenBG进行了预训练，并将其应用于商业场景中多项知识图谱增强的下游任务，验证了十亿级多模态知识在电子商务中的有效性。所有资源与代码已发布于\url{https://github.com/OpenBGBenchmark/OpenBG}。