This talk will discuss (ongoing) research on linking and classifying texts with knowledge graphs (KGs). There are mainly two parts: (i) KG enrichment, identifying out-of-KG mentions through entity linking (and their placement into the KG); (ii) Classification of texts into concepts in a large KG. We will present applications in the clinical domain as examples, e.g., clinical ontologies, including UMLS, SNOMED-CT, and ICD, and the current state of automated clinical coding [1-2], and propose future studies.
Invited Speaker: Hang Dong
 Dong, H., Falis, M., Whiteley, W., Alex, B., Matterson, J., Ji, S., … & Wu, H. (2022). Automated clinical coding: what, why, and where we are?. npj digital medicine, 5(1), 159. https://doi.org/10.1038/s41746-022-00705-7
 Dong, H., Suárez-Paniagua, V., Whiteley, W., & Wu, H. (2021). Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation. Journal of Biomedical Informatics, 116, 103728. https://arxiv.org/abs/2010.15728