Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)
Siyuan Cheng, Xiaozhuan Liang, Zhen Bi, Huajun Chen, Ningyu Zhang
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.
BibTeX
@article{Cheng_Liang_Bi_Chen_Zhang_2024, title={Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26955}, DOI={10.1609/aaai.v37i13.26955}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cheng, Siyuan and Liang, Xiaozhuan and Bi, Zhen and Chen, Huajun and Zhang, Ningyu}, year={2024}, month={Jul.}, pages={16190-16191} }