*The First Wikidata Workshop*

Co-located with the 19th International Conference on Semantic Web (ISWC 2020). Date: October 29, 2020 The workshop will be held online, afternoon European time.

Website: https://wikidataworkshop.github.io/

== Important dates ==

Papers due: August 10, 2020 Notification of accepted papers: September 11, 2020 Camera-ready papers due: September 21, 2020 Workshop date: October 29, 2020

== Overview ==

Wikidata is an openly available knowledge base, hosted by the Wikimedia Foundation. It can be accessed and edited by both humans and machines and acts as a common structured-data repository for several Wikimedia projects, including Wikipedia, Wiktionary, and Wikisource. It is used in a variety of applications by researchers and practitioners alike. In recent years, we have seen an increase in the number of publications around Wikidata. While there are several dedicated venues for the broader Wikidata community to meet, none of them focuses on publishing original, peer-reviewed research. This workshop fills this gap - we hope to provide a forum to build this fledgling scientific community and promote novel work and resources that support it. The workshop seeks original contributions that address the opportunities and challenges of creating, contributing to, and using a global, collaborative, open-domain, multilingual knowledge graph such as Wikidata. We encourage a range of submissions, including novel research, opinion pieces, and descriptions of systems and resources, which are naturally linked to Wikidata and its ecosystem, or enabled by it. What we’re less interested in are works which use Wikidata alongside or in lieu of other resources to carry out some computational task - unless the work feeds back into the Wikidata ecosystem, for instance by improving or commenting on some Wikidata aspect, or suggesting new design features, tools and practices. We also encourage submissions on the topic of Abstract Wikipedia, particularly around collaborative code management, natural language generation by a community, the abstract representation of knowledge, and the interaction between Abstract Wikipedia and Wikidata on the one, and Abstract Wikipedia and the language Wikipedias on the other side. We welcome interdisciplinary work, as well as interesting applications which shed light on the benefits of Wikidata and discuss areas of improvement. The workshop is planned as an interactive half-day event, in which most of the time will be dedicated to discussions and exchange rather than frontal presentations. For this reason, all accepted papers will be presented in short talks and accompanied by a poster. We are considering online options in response to ongoing challenges such as travel restrictions and the recent Covid-19 pandemic.

== Topics ==

Topics of submissions include, but are not limited to:

- Data quality and vandalism detection in Wikidata - Referencing in Wikidata - Anomaly, bias, or novelty detection in Wikidata - Algorithms for aligning Wikidata with other knowledge graphs - The Semantic Web and Wikidata - Community interaction in Wikidata - Multilingual aspects in Wikidata - Machine learning approaches to improve data quality in Wikidata - Tools, bots and datasets for improving or evaluating Wikidata - Participation, diversity and inclusivity aspects in the Wikidata ecosystem - Human-bot interaction - Managing knowledge evolution in Wikidata - Abstract Wikipedia

== Submission guidelines ==

We welcome the following types of contributions: - Full research paper: Novel research contributions (7-12 pages) - Short research paper: Novel research contributions of smaller scope than full papers (3-6 pages) - Position paper: Well-argued ideas and opinion pieces, not yet in the scope of a research contribution (6-8 pages) - Resource paper: New dataset or other resource directly relevant to Wikidata, including the publication of that resource (8-12 pages) - Demo paper: New system critically enabled by Wikidata (6-8 pages)

Submissions must be as PDF or HTML, 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. The papers will be peer-reviewed by at least two researchers. Accepted papers will be published as open access papers on CEUR (we only publish to CEUR if the authors agree to have their papers published).

Papers have to be submitted through easychair: https://easychair.org/conferences/?conf=wikidataworkshop2020

== Proceedings ==

The complete set of papers will be published with the CEUR Workshop Proceedings (CEUR-WS.org).

== Organizing committee ==

- Lucie-Aimée Kaffee, University of Southampton - Oana Tifrea-Marciuska, Bloomberg - Elena Simperl, King’s College London - Denny Vrandečić, Wikimedia Foundation

== Programme committee ==

- Dan Brickley, Google - Andrew D. Gordon, Microsoft Research & University of Edinburgh - Dennis Diefenbach, University Jean Monet - Aidan Hogan, Universidad de Chile - Markus Krötzsch, Technische Universität Dresden - Edgar Meij, Bloomberg - Claudia Müller-Birn, FU Berlin - Finn Årup Nielsen, Technical University of Denmark - Thomas Pellissier Tanon, Télécom ParisTech - Lydia Pintscher, Wikidata, Wikimedia Deutschland - Alessandro Piscopo, BBC - Marco Ponza, University of Pisa - Simon Razniewski, Max Planck Institute for Informatics - Miriam Redi, Wikimedia Foundation - Cristina Sarasua, University of Zurich - Maria-Esther Vidal, TIB Hannover - Pavlos Vougiouklis, Huawei Technologies, Edinburgh - Zainan Victor Zhou, Google