[Paolo Manghi]

Dear Colleagues,
Happy New Year!
I believe this could be of interest to you.
Regards,
Paolo
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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
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Paolo Manghi
ORCiD: 0000-0001-7291-3210
Web page: https://infrascience.isti.cnr.it/profile/paolo-manghi
InfraScience Lab, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” – CNR
OpenAIRE Infrastructure CTO

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