Fw: [CfP] Sci-K @ISWC 2024 – 4th Int. Workshop on Scientific Knowledge Representation, Discovery, and Assessment

This looks pretty much in scope for SSN. Anyone interested / available to go to Baltimore in November?

Cheers - Simon

dr.shorthair@pm.me
+61 403 302 672

On Boonwurrung land

Sent with [Proton Mail](https://proton.me/) secure email.

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From: Angelo Salatino <aas88ie@gmail.com>
Date: On Tuesday, 30 April 2024 at 17:52
Subject: [CfP] Sci-K @ISWC 2024 – 4th Int. Workshop on Scientific Knowledge Representation, Discovery, and Assessment
To:

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> CALL FOR PAPERS
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> Sci-K – 4th International Workshop on Scientific Knowledge Representation, Discovery, and Assessment in conjunction with the International Semantic Web Conference (ISWC) 2024
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> November 11/12 2024, Baltimore, MD, USA
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> Web: https://sci-k.github.io, X: [@scik_workshop](https://twitter.com/scik_workshop)
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> Submission deadline: July 11th, 2024
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> Aim and Scope:
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> In the last decades, we have experienced a substantial increase in the volume of published scientific articles and research artefacts (e.g., data sets, software packages); this trend is expected to continue and opens up challenges including the development of 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. We foresee three themes that cover the most important challenges in this field: representation, discoverability, and assessment.
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> Representation. There is a need for flexible, context-sensitive, fine-grained, and machine-actionable representations of scholarly knowledge that are, at the same time, structured, interlinked, and semantically rich: Scientific Knowledge Graphs (SKGs). SKGs can power 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.
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> Discoverability. Scholarly information should be easily findable, discoverable, and visible so that it can be mined and organised within SKGs. Discovery tools should be 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 deep understanding of both the scholarly communication landscape and the needs of a variety of stakeholders: researchers (of different fields and sub-fields), 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.
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> Assessment. Due to the continuous growth in volume of research output and limited amounts of funding, rigorous approaches for the evaluation and assessment of research impact are now more relevant than ever. There is a need for 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:
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> Representation
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> Data models for the description of scholarly data and their relationships.
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> Description and use of provenance information of scientific data.
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> Integration and interoperability models of different data sources.
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> NLP and AI approaches that demonstrate related methods and technologies.
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> Discoverability
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> Methods for extracting metadata, entities and relationships from scientific data.
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> Methods for the (semi-)automatic annotation and enhancement of scientific data.
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> Methods and interfaces for the exploration, retrieval, and visualisation of scholarly data.
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> NLP and AI approaches that demonstrate related methods and technologies.
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> Assessment
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> Novel methods, indicators, and metrics for quality and impact assessment of scientific publications, datasets, software, and other relevant entities based on scholarly data.
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> Uses of scientific knowledge graphs and citation networks for the facilitation of research assessment.
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> Studies regarding the characteristics or the evolution of scientific impact or merit.
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> NLP and AI approaches that demonstrate related methods and technologies.
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> Submission Guidelines:
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> Full research papers (up to 8 pages for main content)
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> Short research papers (up to 4 pages for main content)
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> Vision/Position papers (up to 4 pages for main content)
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> 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 or software papers (short or long papers). Data papers present the motivation and methodology behind the creation of data sets that are of value to the community, e.g., annotated corpora, benchmark collections, and training sets. Software papers present software functionality, its value for the community, and its application. To enable reproducibility and peer-review, authors are requested to share the DOIs of datasets and software products described in the articles.
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> The workshop also calls 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. Vision papers do not necessarily have to present results but should carefully elaborate on the motivation and ongoing challenges of the described area.
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> Sci-K will adopt a single-blind review process, and each paper will be reviewed by at least three Program Committee members.
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> Submissions must be in PDF format and must adhere to the CEURART single-column template. Submissions that do not follow these guidelines, or do not view or print properly, may be rejected without review.
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> The proceedings of the workshops will be published on CEUR (indexed in Scopus, DBLP and so on.)
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> Submit your contributions following the link: https://sci-k.github.io/2024/#submission
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> Important Dates:
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> Paper submission: July 11th, 2024 (23:59, AoE timezone)
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> Notification of acceptance: August 8th, 2024
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> Camera-ready due: August 25th, 2024 (23:59, AoE timezone)
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> Workshop day: November 11/12, 2024 (TBA)
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> Organizing Committee (alphabetical order):
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> Andrea Mannocci, CNR-ISTI, Italy
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> Francesco Osborne, The Open University, UK
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> Georg Rehm, DFKI, Germany
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> Angelo Salatino, The Open University, UK
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> Sonja Schimmler, TU Berlin, Fraunhofer FOKUS, Germany