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
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
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
Technical challenges and solutions in community-based KBs,
concerning a technical area such as:
Representation of knowledge and logical foundations
Reasoning, querying, and constraint-checking
Knowledge preparation (e.g., cleaning, deduplication, alignment,
Maintaining consistency with external sources
Representing and managing metadata (including issues involved in
adding metadata to relation instances)
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
Community-based KBs in science, medicine, law, government, or other
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
Tools and affordances supporting community or collaborative
activities, including discussions, feedback, decision making, task
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
Frameworks and approaches to support group decision-making and
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/>.
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-for-authors>. 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-and-promoting-your-article>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&journal_id=3169691>. Final copies of accepted publications will appear in print and at Elsevier's archival online server.
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
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. -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 45009 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20210721/614abdc7/attachment.txt>