Explainable Graph-Based Machine Learning
Virtual Workshop at the 3rd Conference on Automated Knowledge Base Construction (AKBC 2021)
October 8, 2021


Neural models for relational data provide informative representations for knowledge graphs (KGs), modeling missing links, node classes, and errors in the graph. To aid user trust and provide insights into these models, there has been growing interest in investigating the interpretability and robustness of existing knowledge graph representation models. Previous studies provide explanations for graph models' behavior resulting in designing more accurate and robust models. The objectives of XGML workshop are to bring together researchers interested in (a) explaining graph models, (c) adversarial attacks and defenses on graphs, and (b) improving completion and construction of knowledge bases utilizing the insights from explanations, and in general, to share state-of-the-art approaches, best practices, and future directions.


  • Carolin Lawrence, NEC Lab Europe
  • Title: Explainable Knowledge Graph Prediction for Human-AI Collaboration
  • Pascal Hitzler, Kansas State University
  • Title: Knowledge Graphs in Neuro-Symbolic AI
  • Marinka Zitnik, Harvard University
  • Title: Explainable Graph AI for Drug Discovery
  • Freddy Lecue, CortAIx
  • Title: Explaining Deep Neural Networks: The Good, the Bad and the Ugly


    XGML workshop will be held virtually on October 8th from 8:25AM - 12:45PM in Pacific Time (UTC-7).

  • 8:25 AM - 8:30 AM : Opening Remarks
  • 8:30 AM - 9:15 AM : Invited talk: Pascal Hitzler, Knowledge Graphs in Neuro-Symbolic AI
  • 9:15 AM - 10:00 AM : Invited talk: Carolin Lawrence, Explainable Knowledge Graph Prediction for Human-AI Collaboration
  • 10:00 AM - 10:15 AM : Contributed talk: Zhepei Wei, CogKG: Unifying Symbolic and Continuous Knowledge Representations for Machine Reasoning
  • 10:15 AM - 10:30 AM : Contributed talk: Xiangyu Peng, Explainable Reinforcement Learning Agent With Stacked Hierarchical Attention
  • 10:30 AM - 10:45 AM : Break
  • 10:45 AM - 11:30 AM : Invited talk: Marinka Zitnik, Explainable Graph AI for Drug Discovery
  • 11:30 AM - 12:15 PM : Invited talk: Freddy Lecue, Explaining Deep Neural Networks: The Good, the Bad and the Ugly
  • 12:15 PM - 12:30 PM : Contributed talk: Joerg Schad, Some Explainable Graph ML Models with ArangoDB
  • 12:30 PM - 12:45 PM : Closing Remarks


    The workshop on Explainable Graph-Based Machine Learning (XGML) will consist of contributed posters, and invited talks on a wide variety of methods and problems in this area. We invite extended abstract submissions in the following categories to present at the workshop:

  • Providing explanations for different task based on graph models
  • Reasoning and rule extraction for relational data
  • Adversarial attacks and defenses
  • Explainable AI with knowledge graphs
  • Human in the loop
  • Optimization challenges in providing explanations
  • Benchmark datasets and evaluation methods
  • Call for extended abstract

    We invite submission of extended abstracts related to Explainable Graph-Based Machine Learning (XGML). Since the workshop is not intended to have a proceeding comprising full versions of the papers, concurrent submissions to other venues, as well accepted work, are allowed provided that concurrent submissions or intention to submit to other venues is declared to all venues including XGML. Accepted work will be presented as oral during the workshop and listed on this website.

    Reviewing Policy:

    Submissions shall be refereed on the basis of technical quality, potential impact, and clarity. Atleast one of the authors of each accepted submission will be required to present the work virtually.

    Submission instructions

    1). Prepare 1-page abstract.
    2). Please upload your submission in the following Google form (only PDF accepted):
    submission website.
    3). In case of any queries, please drop an email to pezeshkp@uci.edu

    Important dates

    Abstract submission: Aug 20th, 11:59pm PST.
    Acceptance/rejection notification: Sep 10th.
    Workshop: Oct 8th, 2021.


    XGML will be a fully virtual event. You can find the live event here.


    comments powered by Disqus