Call for Papers

4th Conference on Automated Knowledge Base Construction (AKBC)
November 3rd-5th, 2022, Thursday-Saturday (London, UK and online)
Homepage: http://www.akbc.ws
Email: info@akbc.ws

Key dates

  • Paper submission deadline (OpenReview): July 10th
  • Commitment deadline (ARR, with reviews and meta review): August 5th
  • Author response period: August 5th - August 10th
  • Notification of acceptance: September 1st
  • Camera-ready deadline: September 22nd
  • Conference & Workshop Dates: November 3-5th

All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”).

Knowledge Base Construction

Knowledge gathering, representation, and reasoning are among the fundamental challenges of artificial intelligence. Large-scale repositories of knowledge about entities, relations, and their abstractions are known as “knowledge bases”. Most major technology companies now have substantial efforts in knowledge base construction. Related scholarly work spans many research areas, including machine learning, natural language processing, computer vision, information integration, databases, search, data mining, knowledge representation, human computation, human-computer interfaces, and fairness. The AKBC conference serves as a research forum for gathering all these areas, in both academia and industry.

Call for Papers

We invite the submission of papers describing previously unpublished research, including new methodology, datasets, evaluations, surveys, reproduced results, negative results, and visionary positions.

Topics of interest include, but are not limited to:

  • Natural language processing, information extraction, extraction of entities, relations, and events, semantic parsing, coreference, machine reading, entailment, web mining, multilingual NLP.
  • Information integration, entity resolution, schema & ontology alignment, text and structure alignment, federated KBs, Semantic Web.
  • Machine learning, supervised, unsupervised, lightly-supervised and distantly-supervised learning, deep learning, symbolic learning, multimodal learning, embeddings of knowledge.
  • Search, question-answering, reasoning, knowledge base completion, queries on mixtures of structured and unstructured data; querying under uncertainty.
  • Multi-modal knowledge bases: structured data, text, images, video, audio.
  • Human-computer interaction, crowdsourcing, interactive learning.
  • Fairness, accountability, transparency, misinformation, multiple viewpoints, uncertainty.
  • Databases, probabilistic databases, distributed databases, database cleaning, scalable computation, distributed computation, dynamic data, online adaptation of knowledge.
  • Systems, languages and toolkits, demonstrations of existing knowledge bases.
  • Evaluation of AKBC, datasets, evaluation methodology.

Reviewing will be double-blind on the OpenReview platform, with papers, reviews and comments publicly visible. Papers should be restricted to 10 single-column pages, excluding references. Appendices should be put after references and submitted in one PDF document. We also encourage authors to upload their code and data (<=100 Mb) as part of their supplementary material in order to help reviewers assess the quality of the work. Like submissions, supplementary material must be anonymized.

All submissions must be formatted with LaTeX using the following LaTeX source: You can either download the template here or use the Overleaf template.

Submission site: https://openreview.net/group?id=AKBC.ws/2022/Conference.

Submission of previously published/accepted work: Submissions that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, are not allowed and violate our dual submission policy. However, papers that cite previous related work by the authors and papers that have appeared on non-peered reviewed websites (like arXiv) or that have been presented at workshops (i.e., venues that do not have publication proceedings) do not violate the policy. The policy is enforced during the whole reviewing process.

Concurrent Submissions: Concurrent submissions or commitments to other conferences/workshops including EMNLP 2022 is not allowed.

Invited Talks

The following are confirmed invited speakers. Most of them will attend the conference in person and some will present virtually.

Dipanjan Das, Google AI
Jason Eisner, Johns Hopkins University/Semantic Machines
Douwe Kiela, HuggingFace
Partha Pratim Talukdar, Google Research/Indian Institute of Science
Angeliki Lazaridou, Deepmind
Stephan Lewandowsky, University of Bristol
John Winn, Microsoft Research
Raquel Fernández, University of Amsterdam
Jessica D. Tenenbaum, North Carolina Department of Health and Human Services/Duke University
He He, New York University

Workshops

In addition to the conference program, we will have a one-day collection of workshops on focused topics.

Organizers

General Chair
Sebastian Riedel, University College London, Facebook AI Research
Program Co-Chair
Eunsol Choi, UT Austin, Google AI
Program Co-Chair
Andreas Vlachos, University of Cambridge
Workshop Chair
James Thorne, KAIST AI
Virtual Platform Chair
Marek Rei, Imperial College London
Local Chair
Fabio Petroni, Facebook AI Research

Area Chairs

Pasquale Minervini, University College London
Bhavana Dalvi, Allen Institute for Artificial Intelligence
Ioannis Konstas, Heriot-Watt University
Siva Reddy, McGill University
Nicola De Cao, University of Amsterdam
Minjoon Seo, KAIST AI


Questions? Please mail: info@akbc.ws