Hi,
I tried to use the Wikidata LDF endpoint [1] to access label data about
individual entities instead of using the SPARQL endpoint (possibly
unreliable) or looking up URIs directly (which can be very slow when
there are lots of statements about the entity).
But I noticed that the LDF endpoint doesn't know about the language tags
for labels, which makes the service a bit pointless for me, as I'm
interested in the labels in a specific language.
For example, the first example query in the WDQS User Manual section on
the LDF endpoint [2], about the entity Q146 (cat), returns triples like
this:
Q146 label "பூனை"^^http://www.w3.org/1999/02/22-rdf-syntax-ns#langString.
Q146 label "amcic"^^http://www.w3.org/1999/02/22-rdf-syntax-ns#langString.
Q146 label "chat"^^http://www.w3.org/1999/02/22-rdf-syntax-ns#langString.
Q146 label "gat"^^http://www.w3.org/1999/02/22-rdf-syntax-ns#langString.
All the label and description values have the data type rdf:langString
and no language tag. I suspect this is somehow related to RDF 1.1, which
introduced the langString data type.
Any chance of having this fixed or should I just rely on the other
endpoints?
Best,
Osma
[1] https://query.wikidata.org/bigdata/ldf
[2]
https://www.mediawiki.org/wiki/Wikidata_Query_Service/User_Manual#Linked_Da…
--
Osma Suominen
D.Sc. (Tech), Information Systems Specialist
National Library of Finland
P.O. Box 15 (Unioninkatu 36)
00014 HELSINGIN YLIOPISTO
Tel. +358 50 3199529
osma.suominen(a)helsinki.fi
http://www.nationallibrary.fi
Hello Wikidata folks,
I would like to bring your attention to an open source dataset I've been
developing called the Kensho Derived Wikimedia Dataset (KDWD). It's a
cleaned English subset of Wikipedia/Wikidata with 2.3B tokens, 5.3M pages,
51M nodes, and 120M edges. More details are available here
https://blog.kensho.com/announcing-the-kensho-derived-wikimedia-dataset-5d1…
best,
-Gabriel
Dear Wikidata community,
The Knowledge Graph Conference organizing team is pleased to announce the
workshops and tutorials part of the KGC 2020 Program. They are taking place
on May 4 and 5 in Butler Library, Columbia University Libraries in NYC.
Workshops are stand-alone sub events of the conference. They have separate
calls for papers and their own program and organizing committee.
Tutorials are learning sessions including both lecture style and hands-on
sessions. Each tutorial will be for half a day unless specified.
For more information about each workshop and tutorial please visit this
page:
https://www.knowledgegraph.tech/the-knowledge-graph-conference-kgc/workshop…
Early Bird registration ends on *February 15, 2020*. To register please
visit this page:
https://www.knowledgegraph.tech/the-knowledge-graph-conference-kgc/register/
WORKSHOPS
-
KGC Workshop on Applied Knowledge Graph: Best industry/academic
practices, methods and challenges between representation and reasoning
Organizers:
Vivek Khetan, AI research specialist, Accenture Labs, SF
Colin Puri, R&D Principal - Accenture Labs
Lambert Hogenhout, Chief Analytics, Partnerships and Innovation, United
Nations
Limit: 40 people
Date: May 4, 2020
Place: Room 203, Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Personal Health Knowledge Graphs (PHKG): Challenges and Opportunities
Organizers:
Ching-Hua Chen, PhD
<https://researcher.watson.ibm.com/researcher/view.php?person=us-chinghua>,
Amar
Das, MD PhD
<https://researcher.watson.ibm.com/researcher/view.php?person=us-amardas>, Ying
Ding, PhD <https://www.ischool.utexas.edu/tags/ying-ding>, Deborah
McGuinness, PhD <https://tw.rpi.edu/web/person/Deborah_L_McGuinness>, Oshani
Seneviratne, PhD
<https://idea.rpi.edu/people/staff/oshani-seneviratne>, and Mohammed
J Zaki, PhD <http://www.cs.rpi.edu/~zaki>
Limit: 40 people
Date: May 5, 2020
Place: Room 203, Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
TUTORIALS
-
Virtualized Knowledge Graphs for Enterprise Applications
Presenter: Eric Little, PhD – CEO LeapAnalysis
Limit: 20 people
Date and time: May 4, 2020 8:30AM - 12:30PM
Place: Studio Butler, Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Data discovery on a (free) hybrid BI/Search/Knowledge graph platform:
the Siren Community Edition hands on tutorial
Presenter: Giovanni Tummarello, Ph.D
Limit: 20 people
Date and time: May 4, 2020 8:30AM - 12:30PM
Place: Room 523 Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Building a Knowledge Graph from schema.org annotations
Presenters: Elias Kärle, Umutcan Simsek, and Dieter Fensel (STI Innsbruck,
University of Innsbruck)
Limit: 25 people
Date and time: May 4, 2020 1:30PM - 5:30PM
Place: Room 523 Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Designing and Building Enterprise Knowledge Graphs from Relational
Databases
Presenter: Juan Sequeda, DataWorld
Limit: 25 people
Date and time: May 5, 2020 8:30AM - 12:30PM
Place: Room 523 Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Rapid Knowledge Graph development with GraphQL and RDF databases
Presenters: Vassil Momtchev, Ontotext
Limit: 25 people
Date and time: May 5, 2020 1:30PM - 5:30PM
Place: Room 523 Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Introduction to Logic Knowledge Graphs, Succinct Data Structures and
Delta Encoding for Modern Databases, and the Web Object Query Language
Presenter: Dr. Gavin Mendel-Gleason and Cheukting Ho (DataChemist)
Limit: 20 people
Date and time: May 5, 2020 8:30AM - 12:30PM
Place: Room 306 Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
-
Modeling Evolving Data in Graphs While Preserving Backward
Compatibility: The Power of RDF Quads
Presenter: Souripriya Das, Matthew Perry, and Eugene I. Chong (Oracle)
Limit: 20 people
Date and time: May 5, 2020 1:30PM - 5:30PM
Place: Room 306 Butler Library, Columbia University
<https://goo.gl/maps/7ijLP7ze7Jw94uid9>
Violeta Ilik
KGC 2020 Workshops & Tutorials Chair
--
Violeta Ilik
Hello all,
As announced last month, we’ve been working on mismatched reference
<https://www.wikidata.org/wiki/Wikidata:Mismatched_reference_notification_in…>,
a new feature that alerts users when editing a value without changing the
existing attached reference. This feature has been tested over the past
month. Based on the positive feedback we received, we are now able to move
forward and enable the feature on wikidata.org. This will take place this
week, in two different steps:
- Today around 13:00 UTC, you will be able to see a notification
(similar to the constraint ones) after saving an edit
- On Thursday, February 6th, we will also enable a button that will
allow you to hide the notification if you think that the reference is not
mismatched
Please note that for now, the feature is not persistent: the editor who
made the change will see it appear when they saved their edit, but if they
reload the page, the notification will be gone. Other users also won’t be
able to see it. We are considering adding this persistency feature in the
future.
If you want to give feedback about the feature, feel free to use this talk
page
<https://www.wikidata.org/wiki/Wikidata_talk:Mismatched_reference_notificati…>.
If you want to report an issue directly on Phabricator, feel free to use
this form
<https://phabricator.wikimedia.org/maniphest/task/edit/form/43/?projects=wik…>.
Cheers,
--
Léa Lacroix
Project Manager Community Communication for Wikidata
Wikimedia Deutschland e.V.
Tempelhofer Ufer 23-24
10963 Berlin
www.wikimedia.de
Wikimedia Deutschland - Gesellschaft zur Förderung Freien Wissens e. V.
Eingetragen im Vereinsregister des Amtsgerichts Berlin-Charlottenburg unter
der Nummer 23855 Nz. Als gemeinnützig anerkannt durch das Finanzamt für
Körperschaften I Berlin, Steuernummer 27/029/42207.
**************************************************************
KG-BIAS 2020 – Bias in Automatic Knowledge Graph Construction:
A Workshop at AKBC 2020
UC Irvine, USA – Wed June 24, 2020
https://kg-bias.github.io/
kg-bias(a)googlegroups.com
**************************************************************
### Overview
Knowledge Graphs (KGs) store human knowledge about the world in structured
format, e.g., triples of facts or graphs of entities and relations, to be
processed by AI systems. In the past decade, extensive research efforts
have gone into constructing and utilizing knowledge graphs for tasks in
natural language processing, information retrieval, recommender systems,
and more. Once constructed, knowledge graphs are often considered as “gold
standard” data sources that safeguard the correctness of other systems.
Because the biases inherent to KGs may become magnified and spread through
such systems, it is crucial that we acknowledge and address various types
of bias in knowledge graph construction.
Such biases may originate in the very design of the KG, in the source data
from which it is created (semi-)automatically, and in the algorithms used
to sample, aggregate, and process that data.
Causes of bias include systematic errors due to selecting non-random items
(selection bias), misremembering certain events (recall bias), and
interpreting facts in a way that affirms individuals' preconceptions
(confirmation bias). Biases typically appear subliminally in expressions,
utterances, and text in general and can carry over into downstream
representations such as embeddings and knowledge graphs.
This workshop – to be held for the first time at AKBC 2020 – addresses the
questions: “how do such biases originate?”, “How do we identify them?”, and
“What is the appropriate way to handle them, if at all?”. This topic is
as-yet unexplored and the goal of our workshop is to start a meaningful,
long-lasting dialogue spanning researchers across a wide variety of
backgrounds and communities.
Topics of interest include, but are not limited to:
* Ethics, bias, and fairness
* Qualitatively and quantitatively defining types of bias
* Implicit or explicit human bias reflected in data people generate
* Algorithmic bias represented in learned models or rules
* Taxonomies and categorizations of different biases
* Empirically observing biases
* Measuring diversity of opinions
* Language, gender, geography, or interest bias
* Implications of existing bias to human end-users
* Benchmarks and datasets for bias in KGs
* Measuring or remediating bias
* De-biased KG completion methods
* Algorithms for making inferences interpretable and explainable
* De-biasing or post-processing algorithms
* Creating user awareness on cognitive biases
* Ethics of data collection for bias management
* Diversification of information sources
* Provenance and traceability
### Submission Instructions
Submission files should not exceed 8 pages with additional pages allowed
for references. Reviews are double-blind; author names and affiliations
must be removed. All submissions must be written in English and submitted
as PDF files formatted using the sigconf template:
https://www.acm.org/publications/proceedings-template.
Submissions should be made electronically through
https://easychair.org/conferences/?conf=kgbias2020.
### Workshop format
We accept position papers, short papers, and full papers. Both ongoing and
already published work is welcomed, and we will offer authors the option of
having their paper included in the workshop proceedings. More details
regarding the actual format and schedule of the workshop will be announced
closer to the workshop date.
### Important Dates
Apr 27
KG-BIAS 2020 submission deadline
May 18
KG-BIAS 2020 notification
Jun 22-23
AKBC Conference
Jun 24
KG-BIAS 2020 workshop
### Code of Conduct
Our workshop adheres to all principles and guidelines specified in the ACM
Code of Ethics and Professional Conduct <https://www.acm.org/code-of-ethics>
.
### Organizing committee
* Edgar Meij, Bloomberg
* Tara Safavi, University of Michigan
* Chenyan Xiong, Microsoft Research AI
* Miriam Redi, Wikimedia Foundation
* Gianluca Demartini, University of Queensland
* Fatma Özcan, IBM Research
### Contact information
You can find us at https://kg-bias.github.io/ and contact us at
kg-bias(a)googlegroups.com.