Knowledge Graphs at Scale

Knowledge graphs (KGs) are a flexible knowledge representation paradigm intended to allow knowledge to be consumed by humans and machines. KGs are widely regarded as a key enabler for a number of increasingly popular technologies including Web search, question answering, personal assistants and AI across most sectors including Industry 4.0, personalized medicine, legislation, economics and more. KGs are now used by several large companies as a key component of their data products. However, while they are rightly praised as a key technology for all future data-driven enterprises and regarded as a promising approach towards “blurring the lines between human and machine”, KGs are currently unattainable for the majority of companies and users.
The objective of KnowGraphs is to scale KGs to be accessible to a wide audience of users across multiple domains including companies (in domains including Industry 4.0, biomedicine, finance, law) of all sizes and even end users (e.g., through personal assistants and web search). Addressing this goal demands a mix of works from the theoretical foundations to the exploitation and economic repercussions of knowledge graphs.

Operating Time: 10/2019 - 09/2023

Source of Funding: EU Horizon 2020


Web Site: KnowGraphs


  1. Xiong, B., Potyka, N., Tran, T.-K., Nayyeri, M., & Staab, S. (2022). Faithful Embedding for EL++ Knowledge Bases. Proceedings of the 21st International Semantic Web Conference (ISWC 2022), 1–16.
  2. Xiong, B., Zhu, S., Potyka, N., Pan, S., Zhou, C., & Staab, S. (2022). Pseudo-Riemannian Graph Convolutional Networks. Advances in Neural Information Processing Systems.
  3. Xiong, B., Cochez, M., Nayyeri, M., & Staab, S. (2022). Hyperbolic Embedding Inference for Structured Multi-Label Prediction. Advances in Neural Information Processing Systems 2022.
  4. Xiong, B., Zhu, S., Nayyeri, M., Xu, C., Pan, S., Zhou, C., & Staab, S. (2022, August). Ultrahyperbolic Knowledge Graph Embeddings. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-22.
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