This workshop aims to invite researchers, practitioners, and the general public to brainstorm the ideas related to Open Knowledge Network (OKN).
Explainable AI in health is in its early stages and can be more complex given the highly heterogeneous real-world clinical setting with inbuilt uncertainty.
Promoting research and applications of KGs related to enhancing, scalability, interpretability of insights generated by deep learning.
Seeking original contributions describing theoretical and practical methods and techniques for building and maintaining personal health knowledge graphs.
Seeking original contributions describing theoretical and practical methods and techniques for building and maintaining personal health knowledge graphs.
The organizing committee of this workshop contains leading scientists from semantic web, data mining, graph mining, artificial intelligence, and the applied area of healthcare. e graphs.
Data scientist professionals in various fields shared the latest research and applications of data science in a broad set of domains.
Knowledge graphs have been widely applied in drug discovery, fraud detection, healthcare, financial intelligence, business intelligence, chatbot, virtual assistant, and robots.
The local event in Austin will have a special session, The AI Health Data Challenge will be kicked off by UT Austin and will be open to data science community regardless of gender.
Fostered discussions along with topics about Knowledge Graphs.
