Pursuant to prior discussions about the need for a research
policy on Wikipedia, WikiProject Research is drafting a
policy regarding the recruitment of Wikipedia users to
participate in studies.
At this time, we have a proposed policy, and an accompanying
group that would facilitate recruitment of subjects in much
the same way that the Bot Approvals Group approves bots.
The policy proposal can be found at:
http://en.wikipedia.org/wiki/Wikipedia:Research
The Subject Recruitment Approvals Group mentioned in the proposal
is being described at:
http://en.wikipedia.org/wiki/Wikipedia:Subject_Recruitment_Approvals_Group
Before we move forward with seeking approval from the Wikipedia
community, we would like additional input about the proposal,
and would welcome additional help improving it.
Also, please consider participating in WikiProject Research at:
http://en.wikipedia.org/wiki/Wikipedia:WikiProject_Research
--
Bryan Song
GroupLens Research
University of Minnesota
Hi all,
Join the Research Team at the Wikimedia Foundation [1] for their monthly
Office hours this Tuesday, 2021-08-03, at 16:00-17:00 UTC (9am PT/6pm
CEST).
To participate, join the video-call via this link [2]. There is no set
agenda - feel free to add your item to the list of topics in the etherpad
[3] (You can do this after you join the meeting, too.), otherwise you are
welcome to also just hang out. More detailed information (e.g. about how to
attend) can be found here [4].
Through these office hours, we aim to make ourselves more available to
answer some of the research related questions that you as Wikimedia
volunteer editors, organizers, affiliates, staff, and researchers face in
your projects and initiatives. Some example cases we hope to be able to
support you in:
-
You have a specific research related question that you suspect you
should be able to answer with the publicly available data and you don’t
know how to find an answer for it, or you just need some more help with it.
For example, how can I compute the ratio of anonymous to registered editors
in my wiki?
-
You run into repetitive or very manual work as part of your Wikimedia
contributions and you wish to find out if there are ways to use machines to
improve your workflows. These types of conversations can sometimes be
harder to find an answer for during an office hour, however, discussing
them can help us understand your challenges better and we may find ways to
work with each other to support you in addressing it in the future.
-
You want to learn what the Research team at the Wikimedia Foundation
does and how we can potentially support you. Specifically for affiliates:
if you are interested in building relationships with the academic
institutions in your country, we would love to talk with you and learn
more. We have a series of programs that aim to expand the network of
Wikimedia researchers globally and we would love to collaborate with those
of you interested more closely in this space.
-
You want to talk with us about one of our existing programs [5].
Hope to see many of you,
Martin on behalf of the WMF Research Team
[1] https://research.wikimedia.org
[2] https://meet.jit.si/WMF-Research-Office-Hours
[3] https://etherpad.wikimedia.org/p/Research-Analytics-Office-hours
[4] https://www.mediawiki.org/wiki/Wikimedia_Research/Office_hours
[5] https://research.wikimedia.org/projects.html
--
Martin Gerlach
Research Scientist
Wikimedia Foundation
Hi everyone,
Summary: We have released two COVID-19 related data-sets. If you just need
the information about the datasets, please read the section titled "Data
release details" below. The rest of this email provides background as well
as thanks and credits information.
==Background==
On March 26, 2020, only a few months into the COVID-19 pandemic, a group of
Wikimedia volunteer researchers and editors as well as a subset of the
Wikimedia Foundation staff interested in the community, data and research
aspect of our work came together to brainstorm COVID-19 related data and
research. One of the themes that emerged from that conversation was that
the Wikimedia Foundation considers releasing more granular COVID-19 related
data.
==Data release details==
Today we’re happy to share with you that we have publicly released two
Wikipedia readership COVID-19 related data-sets. The first dataset is
COVID-19 article page views by country, the second dataset is one hop
navigation where one of the two pages are COVID-19 related. You can find
both data-sets at
https://figshare.com/articles/dataset/COVID-19_Pandemic_Wikipedia_Readershi…
==Thanks and credits==
This release would not have been possible without contributions from the
following people: Joseph Allemandou (Data Engineering), Marcel Ruiz Forns
(Data Engineering), Fabian Kaelin (Research), Isaac Johnson (Research),
Nuria Ruiz (Data Engineering), Leila Zia (Research). We also want to thank
those who participated in the original brainstorming meeting and offered us
their expertise and time [1].
[1] In alphabetical order:
Dan Andreescu, Data Engineering, Wikimedia Foundation
Asaf Bartov, Senior Program Officer, Emerging Wikimedia Communities,
Wikimedia Foundation
Carlos Castillo, Distinguished Research Professor, UPF (User:ChaTo)
Ciro Cattuto, Professor, University of Torino & Research co-Director, ISI
Foundation
Meeyoung Cha, Associate Professor, KAIST
Djellel Difallah, NYU Abu Dhabi (then: Research Scientist, Wikimedia
Foundation)
Martin Gerlach, Research Scientist, Wikimedia Foundation
James Heilman (Doc James), English Wikipedia medical editor
Benjamin Mako Hill, Assistant Professor, University of Washington,
(User:Benjamin Mako Hill)
David Lazer, Professor, Northeastern University
J. Nathan Matias, Assistant Professor, Cornell Communication
Jason Moore, English Wikipedia, WikiProject COVID-19, (User:Another
Believer)
Jonathan Morgan, Senior Design Researcher at CrowdStrike (then: Senior
Design Researcher, Wikimedia Foundation)
Margeigh Novotny, Sr. Dir Product Design & Strategy, Wikimedia Foundation
Sam Patton, Sr. Online Fundraising Manager, Wikimedia Foundation
Miriam Redi, Senior Research Scientist, Wikimedia Foundation
Diego Sáez-Trumper, Senior Research Scientist, Wikimedia Foundation
Jodi Schneider, Assistant Professor, School of Information Sciences, UIUC,
(User:Jodi.a.schneider)
Joseph Seddon, Senior Community Relations Specialist, (User:Seddon)
Markus Strohmaier, Professor, RWTH Aachen University and Sci. Coordinator,
GESIS
Bob West, Assistant Professor, EPFL
Leila Zia, Head of Research, Wikimedia Foundation
Best,
Leila
--
Leila Zia
Head of Research
Wikimedia Foundation
Hi everyone,
We extended the deadline for submissions to the Wikidata Workshop to
*August 6*. We are very much looking forward to your contributions. Please
find more information below.
=======
The Second Wikidata Workshop
Co-located with the 20th International Conference on Semantic Web (ISWC
2021).
Date: October 24 or 25, 2021
The workshop will be held online, afternoon European time.
Website: https://wikidataworkshop.github.io/2021/
== Important dates ==
Papers due: Friday, August 6, 2021 (EXTENDED)
Notification of accepted papers: Friday, September 24, 2021
Camera-ready papers due: Monday, October 4, 2021
Workshop date: October 24/25, 2021
== 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 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.
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 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 exchange rather than oral
presentations. For this reason, all accepted papers will be presented in
short talks and accompanied by a poster. All works will be presented
online.
== 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 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.
The papers will be peer-reviewed by at least three researchers. Accepted
papers will be published as open access papers on CEUR (we will 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=wikidata21
== 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, lucie.kaffee[[(a)]]gmail.com
Simon Razniewski, Max Planck Institute for Informatics, srazniew[[@]]
mpi-inf.mpg.de
Aidan Hogan, University of Chile, ahogan[[(a)]]dcc.uchile.cl
== Programme committee ==
Miriam Redi, Wikimedia Foundation
John Samuel, CPE Lyon
Dennis Diefenbach, University Jean Monet
Lydia Pintscher, Wikimedia Deutschland
Edgar Meij, Bloomberg L.P.
Thomas Pellissier Tanon, Lexistems
Hiba Arnaout, MPI for Informatics
Fabian Suchanek, Télécom ParisTech
Filip Ilievski, ISI
Marco Ponza, Bloomberg L.P.
Heiko Paulheim, University of Mannheim
Cristina Sarasua, University of Zurich
Pavlos Vougiouklis, Huawei Technologies, Edinburgh
Finn Årup Nielsen, Technical University of Denmark
Andrew D. Gordon, Microsoft Research & University of Edinburgh
--
Lucie-Aimée Kaffee
--
Lucie-Aimée Kaffee
Call for Participation
14th Conference on Intelligent Computer Mathematics
- CICM 2021 -
July 26-31, 2021
*Participation on Zoom is FREE after registration*
http://www.cicm-conference.org/2021
----------------------------------------------------------------------
Digital and computational solutions are becoming the prevalent means
for the generation, communication, processing, storage and curation of
mathematical information.
CICM brings together the many separate communities that have developed
theoretical and practical solutions for mathematical applications such
as computation, deduction, knowledge management, and user interfaces.
It offers a venue for discussing problems and solutions in each of
these areas and their integration.
CICM 2021 Programme committee:
see https://www.cicm-conference.org/2021/cicm.php?event=&menu=pc
CICM 2021 submissions are about all topics relating to intelligent
computer mathematics, in particular but not limited to
* theorem proving and computer algebra
* mathematical knowledge management
* digital mathematical libraries
CICM appreciates the varying nature of the relevant research in this
area and presents a mix of submissions of different forms: regular
papers, project and survey papers, system and dataset
descriptions.
See you soon on-line!
Apologies for cross-posting. The full release description including
further statistics can be found on
https://www.dbpedia.org/blog/snapshot-2021-06-release/
<https://www.dbpedia.org/blog/snapshot-2021-06-release/>.
We are pleased to announce immediate availability of a new edition of
the free and publicly accessible SPARQL Query Service Endpoint and
Linked Data Pages, for interacting with the new Snapshot Dataset.
What is the “DBpedia Snapshot” Release?
Historically, this release has been associated with many names: "DBpedia
Core", "EN DBpedia", and — most confusingly — just "DBpedia". In fact,
it is a combination of —
*
EN Wikipedia data— A small, but very useful, subset (~ 1 Billion
triples or 14%) of the whole DBpedia extraction
<https://link.springer.com/chapter/10.1007/978-3-030-59833-4_1>using
theDBpedia Information Extraction Framework
<https://github.com/dbpedia/extraction-framework>(DIEF), comprising
structured information extracted from the English Wikipedia plus
some enrichments from other Wikipedia language editions, notably
multilingual abstracts in ar, ca, cs, de, el, eo, es, eu, fr, ga,
id, it, ja, ko, nl, pl, pt, sv, uk, ru, zh.
*
Links— 62 million community-contributed cross-references and
owl:sameAs links to other linked data sets on the Linked Open Data
(LOD) Cloud that allow to effectively find and retrieve further
information from the largest, decentral, change-sensitive knowledge
graph on earth that has formed around DBpedia since 2007.
*
Community extensions— Community-contributed extensions such as
additional ontologies and taxonomies.
Release Frequency & Schedule
Going forward, releases will be scheduled for the 15th of February, May,
July, and October (with +/- 5 days tolerance), and are named using the
same date convention as the Wikipedia Dumps that served as the basis for
the release. An example of the release timeline is shown below:
June 6–8
June 8–20
June 20–July 10
July 10–20
Wikipedia dumps for June 1 become available on https://dumps.wikimedia.org/
Download and extraction with DIEF
Post-processing and quality-control period
Linked Data and SPARQL endpoint deployment
Data Freshness
Given the timeline above, the EN Wikipediadata of DBpedia Snapshot has a
lag of 1-4 months.
Further Information
Growth of DBpedia, breakdown of links by domain, download instructions
and some tips on how to effectively work with DBpedia are published as
part of this blog post:
https://www.dbpedia.org/blog/snapshot-2021-06-release/
<https://www.dbpedia.org/blog/snapshot-2021-06-release/>
Dear All,
The Centre for Internet & Society (CIS), India is seeking applications
for the position of Research Associate, to support its Access to
Knowledge (A2K) Programme <https://meta.wikimedia.org/wiki/CIS-A2K>.
The research associate is expected to support the larger goals of the
programme, which include growth of Indian language Wikipedias, related
Wikimedia projects and the contributor communities. Your primary
responsibility will be to undertake research on selected topics relevant
to key areas of programmatic work. For the present year, the focus will
be on studying challenges and efforts to bridge the gender gap/bias on
Indian language Wikimedia projects, exploring possibilities of
collaboration with organizations working on building resources related
to gender and sexuality, and assisting with a baseline study on
Wikisource projects in India.
Given present restrictions on movement due to the pandemic, this would
be a remote, full-time position until further notice. Do note that the
position is open only to applicants based in India. We specifically
encourage applications from individuals identifying as women, LGBTQIA+
and of gender non-conforming identity and sexual orientation.
The deadline for applications is Friday, August 6, 2021. Please read the
detailed call for applications here:
https://cis-india.org/jobs/call-for-research-associate-access-to-knowledge-….
Do share among your networks.
best wishes,
sneha
--
P.P Sneha
The Centre for Internet and Society
Bangalore
http://cis-india.org/
Hello all,
The July Research Showcase will take place on July 21, 16:30 UTC (9:30am
PT/ 12:30pm ET/ 18:30pm CEST). The theme is the effects of campaigns to
close content gaps on Wikipedia, and speakers will be Kai Zhu from McGill
University and Isabelle Langrock from the University of Pennsylvania.
Livestream: https://www.youtube.com/watch?v=otN3H-hIImQ
Talk 1
Speaker: Kai Zhu (McGill University, Canada)
Title: Addressing Information Poverty on Wikipedia
Abstract: Open collaboration platforms have fundamentally changed the way
that knowledge is produced, disseminated, and consumed. In these systems,
contributions arise organically with little to no central governance.
Although such decentralization provides many benefits, a lack of broad
oversight and coordination can leave questions of information poverty and
skewness to the mercy of the system’s natural dynamics. Unfortunately, we
still lack a basic understanding of the dynamics at play in these systems
and specifically, how contribution and attention interact and propagate
through information networks. We leverage a large-scale natural experiment
to study how exogenous content contributions to Wikipedia articles affect
the attention that they attract and how that attention spills over to other
articles in the network. Results reveal that exogenously added content
leads to significant, substantial, and long-term increases in both content
consumption and subsequent contributions. Furthermore, we find significant
attention spillover to downstream hyperlinked articles. Through both
analytical estimation and empirically informed simulation, we evaluate
policies to harness this attention contagion to address the problem of
information poverty and skewness. We find that harnessing attention
contagion can lead to as much as a twofold increase in the total attention
flow to clusters of disadvantaged articles. Our findings have important
policy implications for open collaboration platforms and information
networks.
Talk 2
Speaker: Isabelle Langrock (University of Pennsylvania, USA)
Title: Quantifying and Assessing the Impact of Two Feminist Interventions
Abstract: Wikipedia has a well-known gender divide affecting its
biographical content. This bias not only shapes social perceptions of
knowledge, but it can also propagate beyond the platform as its contents
are leveraged to correct misinformation, train machine-learning tools, and
enhance search engine results. What happens when feminist movements
intervene to try to close existing gaps? In this talk, we present a recent
study of two popular feminist interventions designed to counteract digital
knowledge inequality. Our findings show that the interventions are
successful at adding content about women that would otherwise be missing,
but they are less successful at addressing several structural biases that
limit the visibility of women within Wikipedia. We argue for more granular
and cumulative analysis of gender divides in collaborative environments and
identify key areas of support that can further aid the feminist movements
in closing Wikipedia’s gender gaps.
--
Janna Layton (she/her)
Administrative Associate - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>
The Journal of Web Semantics (JWS) invites submissions for a special
issue on Community-based Knowledge Bases and Knowledge Graphs, edited by
Tim Finin, Sebastian Hellmann, David Martin, and Elena Simperl. (contact
email: cbkb(a)cs.umbc.edu <mailto:cbkb@cs.umbc.edu>) Submissions are due
by November 01, 2021. Please see the JWS post here:
http://www.websemanticsjournal.org/2021/06/cfp-community-based-knowledge-ba…
<http://www.websemanticsjournal.org/2021/06/cfp-community-based-knowledge-ba…>
Introduction
Community-based knowledge bases (KBs) and knowledge graphs (KGs) are
critical to many domains. They contain large amounts of information,
used in applications as diverse as search, question-answering systems,
and conversational agents. They are the backbone of linked open data,
helping connect entities from different datasets. Finally, they create
rich knowledge engineering ecosystems, making significant, empirical
contributions to our understanding of KB/KG science, engineering, and
practices. From here forward, we use "KB" to include both knowledge
bases and knowledge graphs. Also, "KB" and "knowledge" encompass both
ontology/schema and data.
Community-based KBs come in many shapes and sizes, but they tend to
share a number of commonalities:
*
They are created through the efforts of a group of contributors,
following a set of agreed goals, policies, practices, and quality norms.
*
They are available under open licenses.
*
They are central to knowledge-sharing networks bringing together
various stakeholders.
*
They serve the needs of a community of users, including, but not
restricted to, their contributor base.
*
Many draw their content from crowdsourced resources (such as
Wikipedia, OpenStreetMap).
Examples of community-based KBs include Wikidata, DBpedia, ConceptNet,
GeoNames, FrameNet, and Yago. This special issue will highlight recent
research, challenges, and opportunities in the field of community-based
KBs and the interaction and processes between stakeholders and the KBs.
We welcome papers on a wide variety of topics. Papers that focus on the
participation of a community of contributors are especially encouraged.
Topics of interest
We are looking for studies, frameworks, methods, techniques and tools on
topics such as the following:
*
The impact of community involvement on characteristics of KBs such
as requirements, design, technology choices, policies, etc. For
example, how are KB characteristics driven by the community and
reflective of the community's needs?
*
Conversely, the impact of KB characteristics on community
involvement. For example, how do changes in these characteristics
affect the participation and behavior of members of the community?
*
Organizational challenges and solutions in developing and managing
community-based KBs.
*
Technical challenges and solutions in community-based KBs,
concerning a technical area such as:
o
Representation of knowledge and logical foundations
o
Reasoning, querying, and constraint-checking
o
Knowledge acquisition
o
Knowledge preparation (e.g., cleaning, deduplication, alignment,
merging)
o
Maintaining consistency with external sources
o
Representing and managing metadata (including issues involved in
adding metadata to relation instances)
o
Provenance
o
Quality assurance
*
User interfaces and experience, both for contributing to the KB and
using it, by different user groups.
*
Implemented metrics and quality tests to guide the community in
improving KG quality and expanding KG coverage.
*
Achieving and managing knowledge diversity, for instance, in the
form of multilinguality, multi-cultural coverage, multiple points of
view, and a diverse and inclusive contributor base.
*
Detecting and avoiding malicious, inappropriate, and misleading
content in community-based KBs.
*
Biases in community-based KBs and their impact on downstream uses of
KB content.
*
Community-based KBs in science, medicine, law, government, or other
domains.
*
Handling specialized types of knowledge (such as commonsense,
probabilistic, or linguistic knowledge) in a community setting.
*
Methods and tools to manage KB evolution, including change
detection, change management, conflict resolution, visualization of
change history.
*
Tools and affordances supporting community or collaborative
activities, including discussions, feedback, decision making, task
allocation, etc.
*
Motivations and incentives affecting community participation.
*
Approaches and metrics for community health, including but not
restricted to community growth or diversity.
*
Roles and participation profiles in communities building and
maintaining KBs.
*
Frameworks and approaches to support group decision-making and
resolve conflicts.
Types of Papers
We invite submission of Research, Survey, Ontology, and System papers,
according to the guidelines given at https://www.jws-volumes.com
<https://www.jws-volumes.com/>.
Submission Guidelines
The Journal of Web Semantics solicits original scientific contributions
of high quality. Following the overall mission of the journal, we
emphasize the publication of papers that combine theories, methods and
experiments from different subject areas in order to deliver innovative
semantic methods and applications. The publication of large-scale
experiments and their analysis is also encouraged to clearly illustrate
scenarios and methods that introduce semantics into existing Web
interfaces, contents and services.
Submission of your manuscript is welcome provided that it, or any
translation of it, has not been copyrighted or published and is not
being submitted for publication elsewhere.
Manuscripts should be prepared for publication in accordance with
instructions given in the JWS guide for authors
<http://www.elsevier.com/journals/journal-of-web-semantics/1570-8268/guide-f…>.
The submission and review process will be carried out using Elsevier's
Web-based EM system
<https://www.editorialmanager.com/JOWS/default.aspx>. Please state the
name of the SI in your cover letter and, at the time of submission,
please select “VSI:CBKB” when reaching the Article Type selection.
Upon acceptance of an article, the author(s) will be asked to transfer
copyright of the article to the publisher. This transfer will ensure the
widest possible dissemination of information. Elsevier's liberalpreprint
policy<https://www.elsevier.com/authors/journal-authors/submit-your-paper/sharing-…>permits
authors and their institutions to host preprints on their web sites.
Preprints of the articles will be made freely accessible viaJWS First
Look
<https://papers.ssrn.com/sol3/JELJOUR_Results.cfm?form_name=journalbrowse&jo…>.
Final copies of accepted publications will appear in print and at
Elsevier's archival online server.
Important Dates
*
Submission deadline: November 1, 2021
*
Author notification: February 7, 2022
*
Minor revisions due: February 21, 2022
*
Major revisions due: March 14, 2022
*
Papers appear on JWS preprint server: May 2, 2022
*
Publication: Fall or Winter 2022
Guest Editors
Tim Finin is the Willard and Lillian Hackerman Chair in Engineering and
a Professor of Computer Science and Electrical Engineering at the
University of Maryland, Baltimore County (UMBC).
Sebastian Hellmann is the head of the “Knowledge Integration and
Language Technologies (KILT)" Competence Center at InfAI, Leipzig. He
also is the executive director and board member of the non-profit
DBpedia Association with over 30 key players
<https://www.dbpedia.org/members/overview/>in the knowledge graph area.
He earned a rank in AMiner’s top 10 of the most influential scholars in
knowledge engineering of the last decade.
David L. Martinis a Research & Development Scientist in Artificial
Intelligence. He has held positions at SRI International, Siri, Inc.,
Apple, Nuance Communications, Samsung Research America, and the
University of California at Santa Cruz. He is a Senior Member of the
Association for the Advancement of Artificial Intelligence, and
currently works as an independent consultant in Silicon Valley, California.
Elena Simperlis professor of computer science at King’s College London,
a Fellow of the British Computer Society and former Turing fellow.
According to AMiner, she is in the top 100 most influential scholars in
knowledge engineering of the last decade, as well as in the Women in AI
2000 ranking. Before joining King’s College, she held positions at the
University of Southampton, as well as in Germany and Austria.
Hi,
We’re preparing for the June 2021 research newsletter and looking for
contributors.
*Because the May issue of the Wikipedia Signpost (whom we're co-publishing
with) had to be canceled, we skipped last month. But we will resume with
this June issue, due out this Sunday. One focus will be papers presented
recently at Wikiworkshop 2021.*
Please take a look at https://etherpad.wikimedia.org/p/WRN202106 and add
your name next to any paper you are interested in covering. Our target
publication
time is 27 June 20:00 UTC. If you can't make this deadline but would like
to cover a particular paper in the subsequent issue, leave a note next to
the paper's entry.
As usual, short notes and one-paragraph reviews are most welcome.
*Highlights:*
- A Brief Analysis of Bengali Wikipedia’s Journey to 100,000 Articles
- Assessing the quality of health-related Wikipedia articles with
generic and specific metrics
- Bridging the Gender Gap: A research study on Indian Language Wikimedia
Communities
- Characterizing Opinion Dynamics and Group Decision Making in Wikipedia
Content Discussions
- Do I Trust this Stranger? Generalized Trust and the Governance of
Online Communities
- Fast Linking of Mathematical Wikidata Entities in Wikipedia Articles
Using Annotation Recommendation
- Inferring Sociodemographic Attributes of Wikipedia Editors:
State-of-the-art and Implications for Editor Privacy
- Information flow on COVID-19 over Wikipedia: A case study of 11
languages
- Language-agnostic Topic Classification for Wikipedia
- Languages of Knowledge Infrastructures: Learnings from Research on
Indian Language Wikimedia Projects
- Negative Knowledge for Open-world Wikidata
- References in Wikipedia: The Editors’ Perspective
- ShExStatements: Simplifying Shape Expressions for Wikidata
- Simple Wikidata Analysis for Tracking and Improving Biographies in
Catalan Wikipedia
- Structural Analysis of Wikigraph to Investigate Quality Grades of
Wikipedia Articles
- The Language of Liberty: A preliminary study
- Towards Ongoing Detection of Linguistic Bias on Wikipedia
- Towards Open-domain Vision and Language Understanding with Wikimedia
- Tracing the Factoids: the Anatomy of Information Re-organization in
Wikipedia Articles
- Wikidata Logical Rules and Where to Find Them
- Wikipedia Editor Drop-Off: A Framework to Characterize Editors'
Inactivity
- WikiShark: An Online Tool for Analyzing Wikipedia Traffic and Trends
*Masssly and Tilman Bayer*
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