Wikidata is an open knowledge base hosted by the Wikimedia Foundation that can be read and edited by both humans and machines. Wikidata acts as the central source of common, open structured data used by Wikipedia, Wiktionary, Wikisource, and others. It is used in a variety of academic and industrial applications.

In recent years, we have seen an increase in the number of scientific publications around Wikidata. While there are a number of venues for the Wikidata community to exchange, none of those publish original research. We want to bridge the gap between these communities and the research events and give the research-focused part of the Wikidata community a venue to meet and exchange information and knowledge.

The Wikidata Workshop 2022 focuses on the challenges and opportunities of working on a collaborative open-domain knowledge graph such as Wikidata, which is edited by an international and multilingual community. We encourage submissions that observe the influence such a knowledge graph has on the web of data, as well as those working on improving this knowledge graph itself. This workshop brings together everyone working around Wikidata in both the scientific field and industry to discuss trends and topics around this collaborative knowledge graph.

What is Wikidata?

Call for Papers

This workshop will have two tracks: Novel Work, and Previously Published Work.

Papers in the Novel Work track will be published as part of the workshop proceedings. The Previously Published Work track is for papers already published in other conferences, giving the community the chance to access and discuss relevant work that has been presented elsewhere as part of the workshop.

Novel Work Track

The papers will be peer-reviewed by at least three researchers. Selected papers will be published on CEUR (we only publish to CEUR if the authors agree to have their papers published).

For the Novel Work track, we will accept papers up to 12 pages (excluding references, contribution of the paper should justify the length of the paper). We invite the following types of papers:

Novel research contributions (7-12 pages)
Novel research contributions of smaller scope than full papers (3-6 pages)
Presenting a novel idea, that is not yet in the scope of a research contribution (6-8 pages)
Presenting a new dataset or other resource, includes the publication of that resource (8-12 pages)
Presenting a system based on research concepts (6-8 pages)

Previously Published Work Track

Published papers will be reviewed by the organising committee in terms of topical fit and prominence of the publication venue. They will not be published as part of the proceedings.

For the Previously Published Work track, we will accept papers with no page limit, prioritizing instead the importance and relevance of the publication. We invite the following types of papers:

Previously published full papers
Previously published datasets or other resources that are important or interesting to the community
Presenting a previously published system based on research concepts


Papers have to be submitted through EasyChair.

We ask authors to declare the track they intend on submitting to. To do so, please add, at the beginning of the "title" field on the EasyChair submission, either the string "[Novel]", for the Novel Work track, or the string "[Published]", for the Previously Published track.

Submission Link: https://easychair.org/conferences/?conf=wikidataworkshop2022

Important Dates

Papers due: Friday, 29 July 2022

Notification of accepted papers: Friday, 23 September 2022

Camera ready papers due: Monday, 3 October 2022

Workshop date: Monday, 24 October 2022

Submission Guidelines

Submissions must be as PDF, formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). For details on the LNCS style, see Springer’s Author Instructions.

Journal extensions

Extended versions of journal papers are invited for submission to a special issue of the Semantic Web Journal.

Schedule Detail

The workshop will take place in the afternoon, European time

Sessions / Papers

Available in due time

Our Speakers

Available Soon



Co-located with ISWC 2022

Online event

Image: Wikimedia Hackathon 2020, CC-BY-SA 4.0


Organizing Committee

Lucie-Aimée Kaffee, University of Southampton. lucie.kaffee[[@]]gmail.com

Lucie-Aimée Kaffee is a postdoctoral research fellow at the University of Copenhagen. She acquired her PhD from the University of Southampton and was previously a research intern at Bloomberg, London, a research fellow at TIB Hannover and software developer in the Wikidata team, Wikimedia Germany. Her research focus is multilingual linked data in collaborative knowledge graphs and natural language processing. Lucie was part of the OC of the Wikidata Workshop co-located with ISWC'20, proceedings chair of ISWC'20 and ISWC'21, OC of AMAR: First International Workshop on Approaches for Making Data Interoperable at SEMANTiCS'19 and participated in the PC of a variety of conferences and workshops.

Simon Razniewski, Max Planck Institute for Informatics, srazniew[[@]]mpi-inf.mpg.de

Simon Razniewski is a senior researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, where he heads the Knowledge Base Construction and Quality research area. His research focuses on methods for knowledge base construction, as well as quality assessment, with applications in Wikidata and beyond. He has held senior roles in program committees of major conferences such as IJCAI'21 (area chair), or ISWC'20 and CIKM'20 (senior PC member). He has held visiting positions at places such as AT&T Labs-Research, the University of Queensland, and UCSD, and his research has been recognized with multiple awards and research grants.

Gabriel Amaral, King's College London, gabriel.amaral[[@]]kcl.ac.uk

Gabriel Amaral is a computer scientist, graduated summa cum laude from the Federal University of Ceará, and a PhD candidate at King's College London. He is part of the Marie Curie European training network Cleopatra, delivering technologies to build and use large-scale, multilingual knowledge graphs. His research tackles the quality of references and the verification of claims found in Wikidata.

Kholoud Saad Alghamdi, King's College London, kholoud.alghamdi[[@]]kcl.ac.uk

Kholoud Saad Alghamdi is a PhD candidate at King's College London. She obtained her master's degree in Computer Science from the University of Southampton. Her PhD project develops an items recommender system for Wikidata editors. Before that, she was lecturer at King Abdulaziz University and worked previously as a data analyst in the industry.

Program Committee

Niel Chah, University of Toronto

Pierre-Henri Paris, CNAM

Filip Ilievski, Information Sciences Institute, USC

John Samuel, CPE Lyon, LIRIS - UMR 5205

David Abián, King's College London

Alessandro Piscopo, BBC

Mahir Morshed, University of Illinois at Urbana-Champaign

Seyed Amir Hosseini Beghaeiraveri, Heriot-Watt University

Dennis Diefenbach, The QA Company

Alasdair Gray, Heriot-Watt University

Lydia Pintscher, Wikimedia Deutschland

Daniel Garijo, Universidad Politécnica de Madrid

Andrew D. Gordon, Microsoft Research and University of Edinburgh

Thomas Pellissier Tanon, Télécom ParisTech

Houcemeddine Turki, Faculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia

Cristina Sarasua, University of Zurich

Luis Galárraga, Inria