Call for Papers: Special Issue on Commonsense Knowledge and Reasoning

Call for papers: Special Issue on

Commonsense Knowledge and Reasoning

Next to being one of the core research topics of AI since its beginnings, machine common sense has recently received new traction, mainly as a consequence of two factors: the recent surge of commonsense benchmarks, and the large success of neural language models on various AI tasks including commonsense question answering. In addition, many diverse structured and semi-structured sources of commonsense knowledge have been created, some of which are integrated in the Semantic Web. Given the gaps in current models and evaluation, such knowledge sources are leveraged to enhance these models and further improve performance on the benchmarks, as well as to enhance explainability and create novel evaluation challenges. Semantic Web sources thus hold the promise to advance the state-of-the-art research on commonsense knowledge and reasoning. This special issue at the Semantic Web Journal seeks original articles describing theoretical and practical methods and techniques focusing on open challenges with capturing commonsense knowledge, reasoning on tasks, and evaluating existing reasoning techniques in novel ways.

Topics relevant to this special issue include, but are not limited to, the following:

  • Representing and Storing Web of Data
  • Creation of new commonsense knowledge sources
  • Extraction of commonsense knowledge from text, images, or videos
  • Selecting commonsense knowledge from existing Semantic Web sources
  • Integration of existing commonsense knowledge sources
  • Integration of commonsense sources in the Linked Open Data cloud
  • Exploration of commonsense knowledge sources
  • Representation of commonsense knowledge
  • Domain ontologies of commonsense knowledge
  • Axiomatization of commonsense dimensions
  • Impact of commonsense knowledge on downstream tasks
  • Methods for including commonsense knowledge in downstream tasks
  • Using language models for commonsense reasoning
  • Probing for knowledge needs in downstream tasks
  • Evaluation metrics for machine common sense
  • Novel machine common sense tasks
  • Explainable commonsense reasoning
  • Multimodal commonsense reasoning
  • Domain-specific commonsense reasoning
  • Interactive elicitation or evaluation of commonsense knowledge
  • Identifying gaps in commonsense knowledge sources
  • Completion of commonsense knowledge sources


  • Submission deadline: 20th of October 2021. Papers submitted before the deadline will be reviewed upon receipt.

Author Guidelines

Submissions shall be made through the Semantic Web journal website at Prospective authors must take notice of the submission guidelines posted at

We welcome five main types of submissions: (i) full research papers, (ii) reports on tools and systems, (iii) descriptions of ontologies, (iv) dataset descriptions, and (v) survey articles. The description of the submission types is posted at While there is no upper limit, paper length must be justified by content.

Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Commonsense Knowledge and Reasoning" special issue. All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.

Also note that the Semantic Web journal is open access.

Finally please note that submissions must comply with the journal’s Open Science Data requirements, which are detailed in the corresponding blog post.

Guest editors

The guest editors can be reached at .

Filip Ilievski, Information Sciences Institute, University of Southern California, CA, USA

Antoine Bosselut, Stanford University, CA, USA

Kenneth Forbus, Northwestern University, IL, USA

Simon Razniewski, Max Planck Institute for Informatics, Germany

Vered Shwartz, Allen Institute for AI and University of Washington, WA, USA

Guest editorial board

to be expanded

Maria Chang, IBM Research, CA, USA

Marieke van Erp, KNAW Humanities Cluster, The Netherlands

Manas Gaur, University of South Carolina, SC, USA

Leilani Gilpin, Sony AI, USA

Bill Yuchen Lin, University of Southern California, CA, USA

Pavan Kapanipathi, IBM T J Watson Research Center, NY, USA

Kaixin Ma, Carnegie Mellon University, PA, USA

Alessandro Oltramari, Bosch Research, PA, USA

Julien Romero, Max Planck Institute for Informatics, Germany

Ilaria Tiddi, Vrije Universiteit Amsterdam, The Netherlands

Aparna Varde, Montclair State University, NJ, USA

Hongming Zhang, The Hong Kong University of Science and Technology (HKUST), Hong Kong