AKBC 2021 : 3rd Conference on Automated Knowledge Base Construction (AKBC)
Call For Papers
3rd Conference on Automated Knowledge Base Construction (AKBC)
October 4-7, 2021, Monday-Thursday (held virtually)
Paper submission deadline: Thursday, June 17, 2021
Notification of acceptance: Wednesday, July 30, 2021
Conference & Workshop Dates: Monday-Thursday, October 4-7, 2021
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.
About the Conference
Nearly a decade after the first AKBC workshop in Grenoble, France, 2010, AKBC became a conference in 2019. Why a stand-alone conference?
Long-standing and growing interest in the area.
We want to grow and connect the community beyond existing individual conference communities, bringing together ML, NLP, DB, IR, KRR, semantics, reasoning, common sense, QA, human computation, dialog, and HCI.
We want to set our own culture, including reviewing practices, and meeting format.
Why now? Growing interest across many areas. Disconnect among multiple relevant communities. Growing industry and government interest. Many of the long-existing conferences have grown uncomfortably large; a new, smaller conference can be more intimate, hospitable, and supportive.
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We invite the submission of papers describing previously unpublished research, including new methodology, datasets, evaluations, surveys, reproduced results, negative results, and visionary positions.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.
Authors of accepted papers will have the option for their conference paper to be archival (with full text in AKBC Proceedings, and be considered for best paper awards) or non-archival (listed in AKBC Conference schedule, with full text in OpenReview, and the flexibility to also submit elsewhere). Double-blind reviewing will be performed on the OpenReview platform, with papers, reviews and comments publicly visible.
Papers should be restricted to 10 single-column pages, excluding references. Submission site: http://www.akbc.ws/2021/submission.
Dual Submission Policy: 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.
The following are confirmed invited speakers. Additional speakers are expected to be added.
Peter Clark (Allen Institute for AI)
Jia Deng (Princeton)
Greg Durrett (University of Texas Austin)
Yolanda Gil (USC)
Hanna Hajishirzi (University of Washington)
Tim Kraska (MIT)
Monica Lam (Stanford)
Devi Parikh (Georgia Tech and Facebook AI Research)
Sujith Ravi (Amazon Alexa AI)
Siva Reddy (McGill)
Dafna Shahaf (Hebrew University)
David Sontag (MIT)
In addition to the conference program, we will have a one-day collection of workshops on focused topics.
General Co-chair Andrew McCallum, University of Massachusetts Amherst, USA
General Co-chair Sameer Singh, University of California, Irvine, USA
Program Co-chair Danqi Chen, Princeton University
Program Co-chair Jonathan Berant, Tel Aviv University / Allen Institute for AI
Workshop Co-chair Eunsol Choi, UT Austin
Workshop Co-chair Waleed Ammar, Google
Virtual Platform Chair Matt Gardner, Allen Institute for AI, USA
Website Chair Maor Ivgi, Tel Aviv University
Yael Amsterdamer Bar-Ilan University
Bhavana Dalvi Allen Institute for Artificial Intelligence
Greg Durrett UT Austin
William L. Hamilton Mila, McGill University
Robin Jia Facebook AI Research / University of Southern California
Gerard de Melo Hasso Plattner Institute / University of Potsdam
Barbara Plank IT University of Copenhagen
Alex Ratner University of Washington
Partha Talukdar Indian Institute of Science, Bangalore
Chenhao Tan University of Chicago
Jesse Thomason University of Southern California
Andreas Vlachos University of Cambridge
Diyi Yang Georgia Institute of Technology
Questions? Please e-mail email@example.com.