Hello all!
The Search Platform Team usually holds an open meeting on the first
Wednesday of each month. Come talk to us about anything related to
Wikimedia search, Wikidata Query Service (WDQS), Wikimedia Commons Query
Service (WCQS), etc.!
Feel free to add your items to the Etherpad Agenda for the next meeting.
Details for our next meeting:
Date: Wednesday, May 3, 2023
Time: 15:00-16:00 UTC / 08:00 PDT / 11:00 EDT / 17:00 CEST
Etherpad: https://etherpad.wikimedia.org/p/Search_Platform_Office_Hours
Google Meet link: https://meet.google.com/vgj-bbeb-uyi
Join by phone: https://tel.meet/vgj-bbeb-uyi?pin=8118110806927
Have fun and see you soon!
Guillaume
--
*Guillaume Lederrey* (he/him)
Engineering Manager
Wikimedia Foundation <https://wikimediafoundation.org/>
We are very excited that we will conduct the fourth Wikidata workshop this
year and would be very happy to see a lot of submissions from the Wikidata
community! Please see the call for papers below!
The Fourth Wikidata Workshop
Call for Papers
Co-located with the 22nd International Conference on Semantic Web (ISWC
2023).
Date: November 6 or 7, 2023
The format of the workshop will be announced soon
Website: https://wikidataworkshop.github.io/2023/
== Important dates ==
Papers due: Thursday, 20 July 2023
Notification of accepted papers: Thursday, September 31, 2023
Camera-ready papers due: Thursday, September 7, 2023
Workshop date: November 06/07, 2023
== 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 primarily 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 are less
interested in are works that 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.
This year, we again added a track for already published work. To foster
conversations around the topic of Wikidata, we invite authors of papers
published at other conferences to submit their papers to present at the
workshop. These will not be included in the proceedings but gives a chance
for authors to interact with the community.
We welcome interdisciplinary work, as well as interesting applications that
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 exchanges rather than oral
presentations. For this reason, all accepted papers will be presented in
short talks and accompanied by a poster.
== 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 of Wikidata
- Using LLM with Wikidata
- Innovative uses of AI and NLP applications for 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.
= Track 1: Novel Works =
The papers in this track will be peer-reviewed by at least three
researchers using a single-blind review process. Accepted papers will be
published as open-access papers on CEUR (authors can also waive this). We
invite the following types of papers:
- 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 resources 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.
Papers have to be submitted through OpenReview(Please add “[NOVEL]” at the
beginning of the title on the submission page so we know that you are
submitting to this track):
https://openreview.net/group?id=swsa.semanticweb.org/ISWC/2023/Workshop/Wik…
= Track 2: Published works =
This track welcomes papers previously published at a peer-reviewed research
venue to be presented and discussed in the workshop. They do not have to
follow the formatting and page limit instructions from Track 1 and can
instead be submitted in the original format.
Previously published papers will be reviewed by the organising committee in
terms of the topical fit and prominence of the publication venue. They will
not be published as part of the proceedings. We invite the following types
of papers:
- Full research paper: Previously published research contributions
- Resource paper: Previously published datasets or other resources that are
important or interesting to the community
- Demo paper: Presenting a previously published system critically enabled
by Wikidata
Papers have to be submitted through OpenReview (please add “[PUBLISHED]” at
the beginning of the title on the submission page so we know that you are
submitting to this track):
https://openreview.net/group?id=swsa.semanticweb.org/ISWC/2023/Workshop/Wik…
== Proceedings ==
The complete set of papers from the Novel Works Track will be published
with the CEUR Workshop Proceedings (CEUR-WS.org).
== Best Paper Award ==
We will recognize the best paper with the best paper award. Reviewers will
be asked to flag papers they deem worthy of a prize. The general chairs
will set up a small panel that will read the papers, consider the
reviewers' comments and assess the talk to determine the winner. The award
comes with a 500 € prize, sponsored by Robert Bosch GmbH.
== Organizing committee ==
Lucie-Aimée Kaffee, Hasso Plattner Institute, Lucie-Aimee.Kaffee[[(a)]]hpi.de
Simon Razniewski, Bosch Center for AI, Simon.Razniewski[[(a)]]de.bosch.com
Kholoud Alghamdi, King's College London, kholoud.alghamdi[[(a)]]kcl.ac.uk
Hiba Arnaout, Max Planck Institute for Informatics,
harnaout[[(a)]]mpi-inf.mpg.dec
--
Lucie-Aimée Kaffee
--
Lucie-Aimée Kaffee
Hi all,
Too many items on Wikidata still miss the basic statements. Perhaps we can
focus together for a short period of time on a single subject to get this
fixed.
For example: all items with instance of (P31) tourist attraction (like
museums) should also contain the country (P17) where it is located.
In the past day I worked on this and got it down from 4689 to 1190 items,
but help is welcome.
Query:
https://query.wikidata.org/#SELECT%20%3Fitem%20%3FitemLabel%20%3FitemDescri…
Who can help to get this number down further?
Thanks!
Romaine(or