International Workshop
on Knowledge Graph:
Mining Knowledge Graph for Deep Insights


9:00 AM - 6:30 PM. Virtual Conference. Aug 24, 2020

New! Due to the COVID-19 pandemic and many requests to extend the deadline, we would like to give authors more time to prepare their submssions. Our new deadline for submission will be June 5th, 2020.

Confirmed Speakers and Panelists



Michael Atkin

Kim Branson


Shih-Fu Chang

Columbia University

Jonathan Dry


Jiawei Han


James Hendler


Ethan Kim

Google Health

Ora Lassila


Brian Martin


Tom Plasterer


Christopher Ré

Stanford University

David Wild


Marinka Zitnik

Harvard University


Knowledge graphs (KGs) are becoming the foremost driving force to enable the Artificial Intelligence (AI). Like human brains, knowledge graphs will become the brains for machines that can connect dots, perform cognitive inference, and most importantly, derive insights from the vast amount of heterogeneous data. The cutting-edge machine learning and deep learning algorithms can empower machines to detect hidden patterns and build strong memories beyond human imagination, but if the data is siloed (or disconnected), no matter how big it is, it is powerless. Knowledge graph is the necessary step to integrate disparate datasets and build machine processible knowledge to enable intelligent machine learning and deep learning.

Graph data model will replace the relational data model to become the prominent data model to realize the intelligence of AI. Because the relationships of data are critical to understand the complexity about organizations, people, biological entities, and financial transactions. Gartner predicted that knowledge graph application and graph mining will grow 100% annually through 2022 to enable more complex and adaptive data science. In regard to the black box nature of AI algorithms, explainable AI becomes indispensable for applications which demand transparent decision makings. Knowledge graph can play an essential role to decipher the hidden connections and complex contexts into traceable paths. Therefore, knowledge graphs have been widely applied in drug discovery, fraud detection, healthcare, financial intelligence, business intelligence, chatbot, virtual assistant, and robots.

Due to COVID-19 pandemic, we will work closely with KDD conference organizers to investigate feasible options to make this workshop successful.

The workshop will be open for the whole conference. Each paper will be evaluated by three reviewers from the aspects of novelty, significance, technique sound, experiments, and presentations. The reviewers will be program committee members or researchers recommended by the members.

Authors can submit either full papers of 8 pages in length or short papers of 4 pages length in the ACM format (, with the "sigconf" option. Since we plan to follow single-blind review process, there is no need to anonymize the author list.

Publication:   High quality submissions with substantial revisions will be invited to submit to Data Intelligence Journal published by MIT Press (

Please submit your papers at

Topics (not limit to)

Building KGs using NLP

Visual searching and intelligent browsing KGs

Industrial applications of KGs: banking, financing, retail, healthcare, medicine, pharma, etc.

KGs powered machine learning and deep learning

KGs in computer vision, medical imaging

KGs for AI ethics and misinformation

Machine learning, including deep learning, algorithms on KGs

Visualizing KGs

Inferencing on KGs

Intelligent services using KGs: chatbot, virtual assistant

KGs for explainable AI

Semantic web and KGs

Important Dates

(all deadlines are midnight Alofi Time)

June 5, 2020

Submissions due

June 15, 2020

Acceptance notifications

July 1, 2020

Camera-ready submission

Aug 24, 2020

Workshop date


Ying Ding

University of Texas at Austin

Benjamin Glicksberg

Icahn School of Medicine at Mount Sinai

James Hendler


Edgar Meij

United Kingdom

Francois Scharffe

Columbia University

Jie Tang

Tsinghua University

Fei Wang

Cornell University