KDD Workshop on

Knowledge-infused Mining and Learning

Co-located with 26th ACM SIGKDD San Diego Convention Center, San Diego, California, USA Location Proceedings
Updates

News

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Acceptance of KiML @ KDD2020

KiML workshop was accepted at 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

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KiML Website Online

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.

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EasyChair paper submission system

KiML 2020 EasyChair paper submission system is online

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Porgram Committee

We welcome our multidisciplinary program committee.

Why Attend?

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.

The Workshop

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.