Research in artificial intelligence and data science is accelerating rapidly due to an unprecedented explosion in the amount of information on the web. In parallel, we noticed immense growth in the construction and utility of the knowledge network from Google, Netflix, NSF, and NIH.

However, current methods risk an unsatisfactory ceiling of applicability due to shortcomings in bringing homogeneity between knowledge graphs, data mining, and deep learning. In this changing world, retrospective studies for building state-of-the-art AI and Data science systems have raised concerns on trust, traceability, and interactivity for prospective applications in healthcare, finance, and crisis response.

We believe the paradigm of knowledge-infused mining and learning would account for both pieces of knowledge that accrue from domain expertise and guidance from physical models. Further, it will allow the community to design new evaluation strategies that assess robustness and fairness across all comparable state-of-the-art algorithms.