mlsanityData to 7 Oct 2026

Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph

arXiv 2209.15214 ↗First posted 30 Sep 2022cs.AI cs.CL cs.IRcited by 13

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Abstract (arXiv)

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 urlhttps://github.com/OpenBGBenchmark/OpenBG.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 41 of its 65 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

41 of 65 references.

References · 41

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29 more
  1. 2022mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionscited by 185
  2. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 7; 2024: 1; 2025: 2; 2026: 1.

13 papers.

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  1. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  2. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  3. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  4. 2022FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discoverycited by 23
  5. 2023Scene-Driven Multimodal Knowledge Graph Construction for Embodied AIcited by 17
  6. 2023Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunitiescited by 10
  7. 2023EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Datacited by 9
  8. 2024A Usage-centric Take on Intent Understanding in E-Commercecited by 6
  9. 2023MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Modelscited by 4
  10. 2025Decoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensationcited by 2
  11. 2023A Concept Knowledge Graph for User Next Intent Prediction at Alipaycited by 1
  12. 2025Enhancing Knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metricscited by 1
1 more
  1. 2026Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completioncited by 0