KiML workshop was accepted at 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
KiML-KDD website will provide latest updates on Submissions, Workshop Program, and Speakers. For changes in schedule due to COVID-19, please check KDD2020 website.
We welcome our multidisciplinary program committee.
The workshop will bring together researchers and practitioners from both academia and industry who are interested in the creation and use of knowledge graphs in understanding online conversations on crisis response (e.g., COVID-19), public health (e.g., social network analysis for mental health insights), and finance (e.g., mining insights on the financial impact (recession, unemployment) of COVID-19 using twitter or organizational data).
Additionally, we encourage researchers and practitioners from the areas of human-centered computing, interaction and reasoning, statistical relational mining and learning, intelligent agent systems, semantic social network analysis, deep graph learning, and recommendation systems.
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.