Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)

Authors

  • Siyuan Cheng School of Software Technology, Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies
  • Xiaozhuan Liang School of Software Technology, Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies
  • Zhen Bi School of Software Technology, Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies
  • Huajun Chen College of Computer Science and Technology, Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies
  • Ningyu Zhang School of Software Technology, Zhejiang University, Hangzhou, China College of Computer Science and Technology, Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies

DOI:

https://doi.org/10.1609/aaai.v37i13.26955

Keywords:

Knowledge Graph, Protein Science, Gene Ontology

Abstract

Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences, respectively, to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. We also illustrate the potential applications of ProteinKG65 with a prototype. Our dataset can be downloaded at https://w3id.org/proteinkg65.

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Published

2023-09-06

How to Cite

Cheng, S., Liang, X., Bi, Z., Chen, H., & Zhang, N. (2023). Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16190-16191. https://doi.org/10.1609/aaai.v37i13.26955