Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
Shumin Deng, Chengming Wang, Zhoubo Li, Ningyu Zhang, Zelin Dai,, Hehong Chen, Feiyu Xiong, Ming Yan, Qiang Chen, Mosha Chen, Jiaoyan Chen,, Jeff Z. Pan, Bryan Hooi, Huajun Chen

TL;DR
This paper presents the construction of a billion-scale multimodal business knowledge graph derived from Alibaba, demonstrating its applications in e-commerce and releasing resources for community use.
Contribution
It introduces a large-scale, multimodal business knowledge graph with detailed ontology, benchmarks, and demonstrates its effectiveness in downstream business tasks.
Findings
OpenBG contains 2.6 billion triples and 88 million entities.
Pre-trained OpenBG improves performance on business-related tasks.
OpenBG resources and benchmarks are publicly available.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Biomedical Text Mining and Ontologies · Advanced Graph Neural Networks
MethodsOntology
