- From: Angelo Salatino <aas88ie@gmail.com>
- Date: Sat, 6 Jun 2026 21:48:54 +0100
- To: undisclosed-recipients:;
- Message-ID: <CAOmG_3U+w2UafeHH4phjAy5nkEhwXBxq=7zmV7D3CL+fjxkayA@mail.gmail.com>
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CALL FOR PAPERS
Sci-K – 6th International Workshop on Scientific Knowledge Representation,
Discovery, and Assessment in conjunction with the International Semantic
Web Conference (ISWC) 2026
October 25/26 2026, Bari, Italy (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 24th, 2026 (Extended)
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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 pose challenges,
including developing 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 and measure the quality of structured scientific
resources and AI-driven research support. Sci-K provides a forum for
researchers and practitioners from diverse disciplines to present, educate,
and guide research on 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). Even more so, in line with the recent Barcelona Declaration on Open
Research Information, SKGs/RKGs can power data-driven services to navigate,
analyse, and make sense of research dynamics, thus becoming the structural
backbone of model scholarly communication and research intelligence, such
as AI-driven research assistants. Current challenges relate to the design
of ontologies or alternative representation methods that conceptualise
scholarly knowledge, model its representation, both metadata as well as
richer semantic content such as hypotheses, methods, claims, and research
results, and enable exchange. Furthermore, supporting interdisciplinary
knowledge representation and cross-domain alignment across heterogeneous
scientific fields remains a key challenge. Lastly, application domains such
as semantic publishing illustrate how representation approaches can be
operationalised in scholarly communication, while also exposing open
challenges related to usability, adoption, and the balance between
structured and natural language formats.
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 in 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 range of stakeholders:
researchers (across different fields and subfields), publishers, funders,
and the general public. Other challenges include the discovery and
extraction of entities and concepts, the integration of information from
heterogeneous sources, the identification of duplicates, the identification
of connections between entities, and the identification of conceptual
inconsistencies. We are particularly interested in modern systems that
integrate AI, NLP, and LLM technologies, including hybrid human-AI
workflows where automated methods are combined with expert curation and
validation. Lastly, application domains and use cases are needed to better
understand for which concrete research tasks ontologies, knowledge graphs,
and LLMs can effectively support researchers, such as literature
exploration, hypothesis generation, and synthesis of scientific knowledge.
Assessment. Due to the continuous growth in the volume and diversity of
research products, and the global movement around Responsible Research
Assessment reforms (e.g., DORA, CoARA), inclusive approaches to research
evaluation are more relevant than ever. There is a need for reliable,
comprehensive, inclusive and equitable metrics and indicators of the
scientific impact and merit of publications, datasets, research
institutions, individual researchers, and other relevant entities. In
addition, there is a growing need for methods to assess the quality,
reliability, and usefulness of the underlying representations and discovery
systems themselves, including scientific knowledge graphs, ontologies, and
AI-driven discovery tools, in terms of their coverage, accuracy,
interpretability, and support for research tasks.
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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, including rich semantic representations of hypotheses,
methods, claims, and research results.
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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,
including cross-domain and interdisciplinary knowledge alignment
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NLP and AI approaches that demonstrate related methods and
technologies.
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Relevant knowledge graphs and ontologies.
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Hybrid or LLM-based approaches for representation and knowledge graph
engineering.
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Infrastructures and metadata standards aligned with the Barcelona
Declaration to ensure open and sustainable research information.
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Applications of representation approaches in scholarly communication,
including semantic publishing and structured scientific communication.
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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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Hybrid human-AI workflows for discovery, including curation,
validation, and knowledge refinement.
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Methods supporting interdisciplinary discovery and cross-domain
knowledge exploration.
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Applications and use cases demonstrating how ontologies, knowledge
graphs, and LLMs support research tasks, such as literature exploration,
hypothesis generation, and knowledge synthesis.
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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
research output.
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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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Approaches to research assessment aligned with responsible research
evaluation initiatives (e.g., DORA, CoAra). .
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Metrics and frameworks for evaluating the quality, completeness, and
reliability of scientific knowledge representations, including knowledge
graphs and ontologies.
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Evaluation of discovery systems and AI-driven tools, including their
effectiveness, transparency, interpretability, and support for research
tasks.
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Benchmarking and evaluation methodologies for scholarly data
infrastructures and AI-based research support systems (using ontologies,
LLMs, KGs).
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/***** NEW *****/
Exclusive to ISWC 2026 main tracks’ submissions:
We invite you to submit your paper to Sci-K 2026 if it was rejected from
the main tracks (Research, Resource, In-Use), provided that it is in scope
of the workshop. Info on the website: https://sci-k.github.io
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Submission Guidelines:
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Full research papers (up to 12 pages + unlimited pages of appendices and
references)
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Short research papers (up to 6 pages + unlimited pages of appendices and
references)
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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 for creating data sets of value to the community, e.g.,
annotated corpora, benchmark collections, and training sets. Software
papers present the software's functionality, its value to the community,
and its applications. 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 that provide insights
into new or emerging areas, innovative or risky approaches, or 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. We particularly
welcome papers that address the technical challenges of implementing the
principles of the Barcelona Declaration or contribute to the cause of
Responsible Research Assessment.
Sci-K will adopt a single-blind 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/2026/#submission
<https://sci-k.github.io/2025/#submission>
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Important Dates:
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Paper submission: July 24th, 2026 (23:59, AoE timezone)
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Notification of acceptance: August 21st, 2026
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Camera-ready due: September 13th, 2026 (tentative)
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Workshop day: October 25/26, 2026 (TBA)
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Organising Committee (alphabetical order):
Allard Oelen, TIB, DE
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
Received on Saturday, 6 June 2026 20:49:12 UTC