Apologies for cross-posting. The full release description including
further statistics can be found on
https://www.dbpedia.org/blog/snapshot-2021-09-release/
<https://www.dbpedia.org/blog/snapshot-2021-09-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.
News since DBpedia Snapshot 2021-06
<https://www.dbpedia.org/blog/snapshot-2021-06-release/>
*
Release notes are now maintained in the Databus Collection
(https://databus.dbpedia.org/dbpedia/collections/dbpedia-snapshot-2021-09
<https://databus.dbpedia.org/dbpedia/collections/dbpedia-snapshot-2021-09>)
*
Image and Abstract Extractor was improved
*
Work in progress: Smoothing the community issue reporting and
fixing at Github
(https://github.com/dbpedia/extraction-framework/issues/new/choose
<https://github.com/dbpedia/extraction-framework/issues/new/choose>)
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:
September 6–8
Sep 8–20
Sep 20–Oct 10
Oct 10–20
Wikipedia dumps for June 1 become available on
https://dumps.wikimedia.org/ <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-2021-09-release/
<https://www.dbpedia.org/blog/snapshot-2021-09-release/>
Stay tuned and stay safe!
With kind regards,
The DBpedia Association
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.