The Journal of Web Semantics invites submissions for a special issue on Community-based Knowledge Bases and Knowledge Graphs, edited by Tim Finin, Sebastian Hellmann, and David Martin. (Contact email for the special issue: cbkb@cs.umbc.edu.)
DEADLINE EXTENDED: Submissions are due by January 11, 2022.
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:
- Representation of knowledge and logical foundations
- Reasoning, querying, and constraint-checking
- Knowledge acquisition
- Knowledge preparation (e.g., cleaning, deduplication, alignment, merging)
- Maintaining consistency with external sources
- Representing and managing metadata (including issues involved in adding metadata to relation instances)
- Provenance
- 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.
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. The submission
and review process will be carried out using Elsevier's Web-based EM system. 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 liberal preprint policy
permits authors and their institutions to host preprints on their
web sites. Preprints of the articles will be made freely accessible via JWS First Look. Final copies of accepted publications will
appear in print and at Elsevier's archival online server.
Important Dates
- Submission
deadline: January 11, 2022
- Author
notification: April 18, 2022
- Minor
revisions due: May 2, 2022
- Major
revisions due: May 23, 2022
- Papers
appear on JWS preprint server: July 11, 2022
- Publication: 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 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. Martin is a Software Engineer and
Research & Development Scientist in Artificial Intelligence. He has recently joined the staff of the
Wikimedia Foundation, and has previously 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.
- Submission deadline: January 11, 2022
- Author notification: April 18, 2022
- Minor revisions due: May 2, 2022
- Major revisions due: May 23, 2022
- Papers appear on JWS preprint server: July 11, 2022
- Publication: 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 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. Martin is a Software Engineer and Research & Development Scientist in Artificial Intelligence. He has recently joined the staff of the Wikimedia Foundation, and has previously 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.