[DL] [CfP] Sci-K @ISWC 2025 – 5th Intl. Workshop on Scientific Knowledge Representation, Discovery, and Assessment

[Angelo Salatino]

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CALL FOR PAPERS

Sci-K – 5th International Workshop on Scientific Knowledge Representation,
Discovery, and Assessment in conjunction with the International Semantic
Web Conference (ISWC) 2025


November 2/3 2025, Nara, Japan (exact day TBD)

Web: https://sci-k.github.io,

X: @scik_workshop <https://twitter.com/scik_workshop>,

LinkedIn: https://www.linkedin.com/groups/10083235/

Submission deadline: July 11th, 2025

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Aim and Scope:


Recently, we have experienced a massive increase in the volume of
scientific articles and research artefacts (e.g., datasets, models,
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 and
knowledge discoverable and accessible, and designing reliable and
comprehensive metrics to assess scientific impact. Sci-K provides a forum
for researchers and practitioners from different disciplines to present,
educate, and guide research related to scientific knowledge. Three themes
cover the most important challenges in this field:


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), also known as Research Knowledge Graphs
(RKGs). SKGs/RKGs can power data-driven services for navigating, analysing,
and making sense of research dynamics. Current challenges are related to
the design of ontologies or alternative representation methods able to
conceptualise scholarly knowledge, model its representation, and enable
exchange.


Discoverability. Scholarly information should be easily findable,
discoverable, and visible so that it can be mined and organised within
SKGs/RKGs. 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 or open-access repositories.
This is challenging and requires a 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. We are
particularly interested in modern systems that integrate AI, NLP, and LLM
technologies.


Assessment. Due to the continuous growth in the volume of research output,
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:



   -

   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.
      -

      NLP and AI approaches that demonstrate related methods and
      technologies.
      -

      Relevant knowledge graphs and ontologies.
      -

      Hybrid or LLM-based approaches for representation and knowledge graph
      engineering.
      -

   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.
      -

      NLP and AI approaches that demonstrate related methods and
      technologies.
      -

      LLM-based systems for relevant tasks: e.g., hypothesis generation,
      literature review generation and others.
      -

   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.
      -

      NLP and AI approaches that demonstrate related methods and
      technologies.


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Submission Guidelines:

   -

   Full research papers (up to 12 pages + unlimited pages of appendices and
   references)
   -

   Short research papers (up to 6 pages + unlimited pages of appendices and
   references)
   -

   Vision/Position papers (up to 6 pages + unlimited pages of appendices
   and references)


The workshop calls for full research papers, describing original work on
the listed topics, and short papers, 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.


The workshop also calls for vision/position papers 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.


Sci-K will adopt a single-anonymous review process and each paper will be
reviewed by at least three Program Committee members.


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.


The proceedings of the workshops will be published on CEUR (indexed in
Scopus, DBLP and so on.)


Submit your contributions following the link:
https://sci-k.github.io/2025/#submission


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Important Dates:

   -

   Paper submission: July 11th, 2025 (23:59, AoE timezone)
   -

   Notification of acceptance: August 8th, 2025
   -

   Camera-ready due: August 28th, 2025 (tentative)
   -

   Workshop day: November 2/3, 2025 (TBA)



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Organizing Committee (alphabetical order):

Anna Jacyszyn, FIZ Karlsruhe, DE

Andrea Mannocci, CNR-ISTI, IT

Francesco Osborne, The Open University, UK

Georg Rehm, DFKI, DE

Angelo Salatino, The Open University, UK

Sonja Schimmler, TU Berlin, Fraunhofer FOKUS, DE

Lise Stork, University of Amsterdam, NL
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