MK 2022 : 1st Workshop on Modular Knowledge
Call For Papers
*Workshop on Modular Knowledge (1st edition), co-located with ESWC 2022*
*** EXTENDED DEADLINES ***
*Third Call for Papers*
We invite submissions to the 1st Workshop on Modular Knowledge (MK2022), to be held in conjunction with the Extended Semantic Web Conference (ESWC) that will take place in Hersonissos (Greece) from May 29 to June 2, 2022.
The Modular Knowledge workshop offers an interdisciplinary venue for discussing and developing solutions for modularity of knowledge: the dramatic increase in the amount of open and linked data and the increasing semantification of such data make clear that knowledge is not monolithic, static or uniform, and that there is a need of methods and tools for dealing with heterogeneous and distributed knowledge as a constellation of modules.
The workshop aims to cover and establish connections between various approaches (ranging from rich semantic representations, like Knowledge Graphs and formal ontology, to simpler schemas, like RDF and database schemas) for representing knowledge, its context, its evolution, and for making it accessible to automatic reasoning and knowledge management tasks. We welcome approaches that make use of logic-based, subsymbolic, or numerical representations.
Modular Knowledge is a full-day workshop consisting of full paper and short/position paper presentations, a lightning talk session, an interactive session between pairs of participants randomly selected, and an open discussion between all participants.
*Important Dates (UPDATED)*
- Abstract submission deadline (for full or short papers): March 8, 2022
- Paper submission deadline: March 12, 2022
- Paper notification: April 9, 2022
- Camera-ready version: April 23, 2022
- Submission deadline for lightning talks: May 8, 2022
- Workshop date: May 29, 2022
We seek contributions on all aspects of modularity in data, information and knowledge, including:
- Theoretical and cognitive aspects of modularity
- Languages for capturing modularity
- Modularity in knowledge graphs, linked data and ontologies (conceptual as well as formal)
- Modules and modularity at all stages of knowledge engineering, including during modeling and design, formalization, verification, and use (for querying, reasoning and other purposes)
- Extracting and computing modules from knowledge and data sources
- Merging, aligning, integrating, and matching of data and knowledge via modules
- Versioning and evolution of modules and modular knowledge
- Reasoning and representing knowledge in context
We invite the submission of original research results and proposed research directions related to the focus areas of the workshop, in one of the three categories given below:
- Full papers (up to 12 pages including references) with mature work and established results, including research reports and surveys
- Short/position papers (up to 6 pages including references) presenting proposed research directions, new open issues, ideas and challenges, positions and opinions on the status of the field
- Lightning talk abstracts (up to 500 words) with a position statement, a challenge, a project, a tool, a team, a paper/poster/demo presented at the main conference, related to the topics of the workshop
All papers must be formatted using the Springer LNCS style and submitted non-anonymously in PDF via EasyChair at this link: https://easychair.org/conferences/?conf=mk2022
Accepted full and short papers will be published in the workshop proceedings. Accepted lightning talks abstracts will be made available from the workshop website.
The best papers from each workshop may be included in the supplementary proceedings of ESWC 2022, which will appear in the Springer LNCS series.
Further information about paper publication will be soon available at the workshop website.
- Loris Bozzato (Fondazione Bruno Kessler)
- Valentina Anita Carriero (University of Bologna)
- Torsten Hahmann (University of Maine)
- Antoine Zimmermann (École des Mines de Saint-Étienne)
- Web: https://mk2022.fbk.eu/
- Mail: email@example.com