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CALL FOR PAPERS
Sci-K – 3rd International Workshop on Scientific
Knowledge Representation, Discovery, and Assessment in
conjunction with The Web Conference (WWW) 2023
April 30-May 4, 2023, Austin, Texas, USA
web:
https://sci-k.github.io,
twitter: @scik_workshop
Submissions deadline: February 6th, 2023
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Aim and Scope:
In the last decades, we have experienced a substantial
increase in the volume of published scientific articles
and related research objects (e.g., data sets, software
packages); a trend that is expected to continue. This
opens up fundamental challenges including generating
large-scale machine-readable representations of
scientific knowledge, making scholarly data discoverable
and accessible, and designing reliable and comprehensive
metrics to assess scientific impact. The main objective
of Sci-K is to provide a forum for researchers and
practitioners from different disciplines to present,
educate, and guide research related to scientific
knowledge. Specifically, we foresee three main themes
that cover the most important challenges in this field:
representation, discoverability, and assessment.
Representation. There is an urge for flexible,
context-sensitive, fine-grained, and machine-actionable
representations of scholarly knowledge that at the same
time are structured, interlinked, and semantically rich:
Scientific Knowledge Graphs (SKGs). These resources can
power several data-driven services for navigating,
analysing, and making sense of research dynamics.
Current challenges are related to the design of
ontologies able to conceptualise scholarly knowledge,
model its representation, and enable its exchange across
different SKGs.
Discoverability. It is important that scholarly
information is easily findable, discoverable, and
visible, so that it can be mined and organised within
SKGs. Hence, we need discovery tools able to crawl the
Web and identify scholarly data, whether on a
publisher’s website or elsewhere – institutional
repositories, preprint servers, open-access
repositories, and others. This is a particularly
challenging endeavour as it requires a deep
understanding of both the scholarly communication
landscape and the needs of a variety of stakeholders:
researchers, publishers, funders, and the general
public. Other challenges are related to the discovery
and extraction of entities and concepts, integration of
information from heterogeneous sources, identification
of duplicates, finding connections between entities, and
identifying conceptual inconsistencies.
Assessment. Due to the continuous growth in the volume
of research output, rigorous approaches for the
assessment of research impact are now more valuable than
ever. In this context, we urge reliable, comprehensive,
and equitable metrics and indicators of the scientific
impact and merit of publications, datasets, research
institutions, individual researchers, and other relevant
entities.
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Topics of Interest:
- Representation
- Data models for the description of scholarly data
and their relationships.
- Description and use of provenance information of
scientific data.
- Integration and interoperability models of
different data sources.
- Discoverability
- Methods for extracting metadata, entities and
relationships from scientific data.
- Methods for the (semi-)automatic annotation and
enhancement of scientific data.
- Methods and interfaces for the exploration,
retrieval, and visualisation of scholarly data.
- Assessment
- Novel methods, indicators, and metrics for quality
and impact assessment of scientific publications,
datasets, software, and other relevant entities based on
scholarly data.
- Uses of scientific knowledge graphs and citation
networks for the facilitation of research assessment.
- Studies regarding the characteristics or the
evolution of scientific impact or merit.
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Submission Guidelines:
- Full research papers (up to 8 pages for main content)
- Short research papers (up to 4 pages for main content)
- Vision/Position papers (up to 4 pages for main
content)
The workshop calls for full research papers (up to 8
pages + 2 pages of appendices + 2 pages of references),
describing original work on the listed topics, and short
papers (up to 4 pages + 2 pages of appendices + 2 pages
of references), on early research results, new results
on previously published works, demos, and projects. In
accordance with Open Science principles, research papers
may also be in the form of data papers and software
papers (short or long papers). The former present the
motivation and methodology behind the creation of data
sets that are of value to the community; e.g., annotated
corpora, benchmark collections, training sets. The
latter presents software functionality, its value for
the community, and its application to a non-specialist
reader. To enable reproducibility and peer-review,
authors will be requested to share the DOIs of the data
sets and the software products described in the articles
and thoroughly describe their construction and reuse.
The workshop will also call for vision/position papers
(up to 4 pages + 2 pages of appendices + 2 pages of
references) providing insights towards new or emerging
areas, innovative or risky approaches, or emerging
applications that will require extensions to the state
of the art. These do not have to include results
already, but should carefully elaborate about the
motivation and the ongoing challenges of the described
area.
Submissions for review must be in PDF format and must
adhere to the ACM template and format. Submissions that
do not follow these guidelines, or do not view or print
properly, may be rejected without review.
The proceedings of the workshops will be published
jointly with The Web Conference 2023 proceedings.
Submit your contributions following the link:
https://sci-k.github.io/2023/#submission
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Important Dates:
- Paper submission: February 6th, 2023 (23:59, AoE
timezone)
- Notification of acceptance: March 6th, 2023
- Camera-ready due: March 20th, 2023 (23:59, AoE
timezone)
- Workshop day: April 30th or May 1st, 2023 (TBA)
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Organizing Committee (alphabetical order):
Yi Bu, Peking University, China
Ying Ding, University of Texas, Austin, US
Ágnes Horvát, Northwestern University, US
Yong Huang, Wuhan University, China
Meijun Liu, Fudan University, China
Paolo Manghi, ISTI-CNR, Italy
Andrea Mannocci, ISTI-CNR, Italy
Francesco Osborne, The Open University, UK
Daniel Romero, University of Michigan, US
Dimitris Sacharidis, Université Libre De Bruxelles,
Belgium
Angelo Salatino, The Open University, UK
Misha Teplitskiy, University of Michigan, US
Thanasis Vergoulis, “Athena” RC, Greece
Feng Xia, RMIT University, Australia
Yujia Zhai, Tianjin Normal University, China