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KiML 2020 : International Workshop on Knowledge-infused Mining and Learning


When Aug 24, 2020 - Aug 24, 2020
Where San Diego, California, USA
Submission Deadline Jun 2, 2020
Notification Due Jul 2, 2020
Final Version Due Jul 10, 2020
Categories    social good   crisis response   public health   finance

Call For Papers

In conjunction with 26th ACM Conference on Knowledge Discovery and Data Mining (KDD 2020), we are organizing an International Workshop on Knowledge-infused Mining and Learning (KiML) in the domain of Healthcare, Crisis Response, and Finance.

KiML2020 invites 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).

Given the current pandemic situation due to the COVID-19 outbreak, we will be holding KiML in full alignment with the decision of ACM KDD 2020 chairs, with the possibility of complete virtual, full in-person, or a mix of virtual and in-person, for presenters and attendees. We will make certain the quality of the virtual workshop experience through appropriate technologies.

We solicit the submission of papers in the following four categories:

*Regular research papers (9 pages including references) include recent or ongoing research
*Position papers (4-6 pages including references)
*Demo papers (2-4 pages)
*Industry papers (4-9 pages including references) include business cases, research-related to extensive product advertising, and surveys

Additionally, invited speakers may submit an invited paper that the organization will review and comment on. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop.

All submissions should be double-blind and use the standard double-column ACM Proceedings Style for formatting. A high-resolution PDF of the paper should be uploaded to the EasyChair submission site before the paper submission deadline. Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some sets may also be chosen for oral presentation.

Important Dates:

*Paper Submission: June 02, 2020 (23:59, anywhere on earth).
*Author Notification: July 02, 2020.
*Camera-ready Papers Due: July 10, 2020.
*Workshop Day: August 24, 2020.

Topics for research and discussions include (but not limited to):


(a) Computational Behavior Analysis and Learning

(b) Semantic Deep Learning

(c) Knowledge-based Causal Inference

(d) Multimodal Knowledge Graphs Representations and Learning

(e) Ontology-based query answering and reasoning

(f) Trust-based Recommendation

(g) Dynamic Knowledge Graphs

(h) Explainable and Interpretable Data Science

(i) Semantic Reinforcement and Transfer learning

(j) Symbolic knowledge extraction and reasoning over Deep Learning

(k) Biologically and Cognitively inspired neural models

(l) Conversation Mining and Understanding

(m) Knowledge Graph Neural Networks


(a) Intelligent Virtual Assistants

(b) Chatbots

(c) Human-in-loop Computing

(d) Data as a Service

(e) AI as Service

(f) Wisdom of Crowd

For any questions, please contact


*Manas Gaur, AI Institute, University of South Carolina, USA.
*Alejandro (Alex) Jaimes, Dataminr Inc., USA.
*Fatma Özcan, IBM Research, USA
*Srinivasan Parthasarathy, Ohio State University, USA.
*Sameena Shah, JP Morgan, USA.
*Amit Sheth, AI Institute, University of South Carolina, USA.
*Biplav Srivastava, IBM Chief Analytics Office, USA.

Program Committee:

*Nitin Agarwal, University of Arkansas
*Shreyansh Bhatt, Amazon Research
*Carlos Castillo, Universitat Pompeu Fabra, Barcelona, Spain
*Lu Chen, LinkedIn
*Vasilis Efthymiou, IBM Research Almaden
*Cory Henson, Bosch Research
*Utkarshani Jaimini, AI Institute, University of South Carolina
*Pavan Kapinapathi, IBM Research
*Ponnurangam Kumaraguru, IIIT Delhi
*Ugur Kurşuncu, AI Institute, University of South Carolina
*Sarasi Lalithsena, IBM Research
*Chuan Lei, IBM Research
*Huan Liu, Arizona State University
*Sriraam Natarajan, University of Texas at Dallas
*Arindam Pal, Data61, CSIRO, Australia
*Sujan Perera, Amazon Research
*Hemant Purohit, George Mason University
*Louiqa Raschid, University of Maryland
*Valerie Shalin, Wright State University
*Yu Su, Ohio State University
*Krishnaprasad Thirunarayan, Wright State University
*Nikhita Vedula, Ohio State University
*Rajneesh Vijh, Bank of America, North Carolina
*Kunal Verma, Appzen
*Ingmar Weber, QCRI, Qatar
*Ruwan Wickramarachchi, AI Institute, University of South Carolina
*Shweta Yadav, AI Institute, University of South Carolina
*Jinjin Zhao, Amazon Research

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