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 2023 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 (unless authors wish to opt out).

Robert Bosch GmbH has signaled that they would likely sponsor a best paper award over 500 Euro.

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 Openreview (link coming soon).

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 Openreview submission, either the string "[Novel]", for the Novel Work track, or the string "[Published]", for the Previously Published track.

Submission Link: https://openreview.net/group?id=swsa.semanticweb.org/ISWC/2023/Workshop/Wikidata

Important Dates

Papers due: Thursday, 27 July 2023 (Extended)

Thursday, 20 July 2023

Notification of accepted papers: Thursday, 31 August 2023

Camera ready papers due: Thursday, 07 September 2023

Workshop date: 07 November 2023 in Athens, Greece

Submission Guidelines

Submissions must be as PDF, for the [Novel] track formatted in the style of the CEUR Publications format for CEUR workshop proceedings. A template is available at https://www.overleaf.com/read/pwspggxsbdvy. For the [Published] track, no reformatting of the original PDFs is needed.

Schedule Detail

The workshop time is morning EET: Tentatively 8:30am - 12:30pm (EET), 7:30am - 11:30 pm (UK), 11:30pm - 3:30 am (California, US)

All times below in EET

  • 08:30 - 08:40


    Welcome from the organisers, agenda, rules of engagement
  • 08:40 - 09:25

    Keynote 1: Katy Weathington, University of Colorado Boulder

    Queer Identities, Normative Databases: Discussing Queerness On Wikidata

  • 09:25 - 09:45

    Lightning Talks 1

  • 09:45 - 10:05

    Poster Session 1

  • 10:05 - 10:50

    Keynote 2: Andra Waagmeester, Micelio

    Exploring Integration of Wikidata, Wikimedia Commons, and Wikipedia with the Semantic Web

  • 10:50 - 11:10


  • 11:10 - 11:30

    Lightning Talks 2

  • 11:30 - 11:50

    Poster Session 2

  • 11:50 - 12:10

    Lightning Talks 3

  • 12:10 - 12:30

    Poster Session 3

  • 12:30 - 12:40


    Concluding remarks, closing

Sessions / Papers

Lightning talks: Each talk has a hard time limit of 3 minutes.
Poster session: Each poster receives an A0 portrait poster board.

Session 1:

Session 2:

Session 3:

Our Speakers

speaker img

Katy Weathington

University of Colorado Boulder


Queer Identities, Normative Databases: Discussing Queerness On Wikidata

speaker img

Andra Waagmeester



Exploring Integration of Wikidata, Wikimedia Commons, and Wikipedia with the Semantic Web

Acropolis in Athens


Co-located with ISWC 2023

In Athens, Greece, in-person

Image: Acropolis Of Athens Greece 03.jpg, CC-BY-SA 4.0


Organizing Committee

Joint email: wikidata-workshop@googlegroups.com

Lucie-Aimée Kaffee, Hasso Plattner Institute. lucie.kaffee[[@]]gmail.com

Lucie-Aimée Kaffee is a postdoctoral research fellow at the Hasso Plattner Institute. She acquired her PhD from the University of Southampton and was previously a postdoctoral research fellow at CopeNLU, University of Copenhagen, 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.

Simon Razniewski, Bosch Center for AI, Simon.Razniewski[[@]]de.bosch.com

Simon Razniewski is a research scientist in the NLP and Neuro-Symbolic AI group at the Bosch Center for AI. 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).

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.

Hiba Arnaout, Max Planck Institute for Informatics, harnaout[[@]]mpi-inf.mpg.de

Hiba Arnaout is a PhD candidate at Max Planck Institute for Informatics. Her research focus is on searching and curating knowledge bases. She co-created and presented a tutorial on Completeness, Recall, and Negation in Open-World Knowledge Bases at top venues such as VLDB and KR, and participated in the PC of various conferences such as ISWC and IJCAI.

Program Committee

Daniele Metilli, University College London

John Samuel, CPE Lyon

Elisavet Koutsiana, King’s College London

Houcemeddine Turki, University of Sfax

Cristina Sarasua, University of Zurich

Seyed Amir Hosseini Beghaeiraveri, Heriot-Watt University

Thomas Pellissier Tanon, Lexistems

Dennis Diefenbach, The QA Company

Stefan Heindorf, Paderborn University

Cristian Consonni, Eurecat Centre Tecnològic de Catalunya

Pierre-Henri Paris, Télécom Paris

Lydia Pintscher, Wikimedia Deutschland

Hoang Thang Ta, Singapore University of Technology and Design

Daniel Garijo, Universidad Politécnica de Madrdid

Filip Ilievski, University of Southern California

Niel Chah, University of Toronto & Microsoft

Mahir Morshed, University of Illinois at Urbana-Champaign