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<pre>
Preface
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.

The Workshop on Knowledge-infused Mining and Learning for Social Impact (KiML 2020) was centered
around the following thematic components: (a) Data Management: includes resource management,
resource discovery across heterogeneous, and inconsistent data resources. (b) Data Usage: includes
methods and systems for visualization, representations, reasoning, and interaction. (c) Evaluation: will
bring together researchers involved at the intersection of databases, semantic web, information systems,
and AI to create new approaches and tools to benefit a broad range of policymakers (e.g. mental health
professions, education practitioners, emergency responders, and economists).

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 main program of KiML’20 consists of seven papers, selected out of thirteen submissions, covering
topics related to knowledge-enabled feature elicitation, adversarial learning, crisis response, public
health, and COVID-19. We sincerely thank the authors of the submissions as well as the attendees of the
workshop. We wish to thank the members of our program committee for their help in selecting
high-quality papers. Furthermore, we are grateful to Manuela Veloso, Sriraam Natarajan, Jose Ambite, and
Pieter De Leenheer for giving keynote presentations on their recent work on Symbiotic Autonomy,
Human Allied Probabilistic Learning, Biomedical Data Science, and Data Intelligence.


                                               Manas Gaur, Alejandro Jaimes, Fatma Özcan, Srinivasan Parthasarathy,
                                                                   Sameena Shah, Amit Sheth, and Biplav Srivastava
                                                                                                       August 2020



Copyright (c) 2020 held by the author(s). In M. Gaur, A. Jaimes, F. Ozcan, S. Shah, A. Sheth, B. Srivastava, Proceedings of the ACM SIGKDD 2020
Workshop on Knowledge-infused Mining and Learning (KDD-KiML 2020). San Diego, California, USA, August 24, 2020. Use permitted under Creative
CommonsLicense Attribution 4.0 International (CC BY 4.0).

</pre>