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,
discoverability, and assessment.
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.
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.
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.
Sci-K is calling for high-quality submissions around the three main themes of research related to
scientific knowledge: representation, discoverability, and assessment.
Topics of interest include, but are not limited to:
Representation
Data models for the description of scholarly data and their relationships, including rich
semantic representations of hypotheses, methods, claims, and research results.
Description and use of provenance information of scientific data.
Integration and interoperability models of different data sources, including cross-domain
and interdisciplinary knowledge alignment
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.
Infrastructures and metadata standards aligned with the Barcelona Declaration to ensure open
and sustainable research information.
Applications of representation approaches in scholarly communication, including semantic
publishing and structured scientific communication.
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.
Hybrid human-AI workflows for discovery, including curation, validation, and knowledge
refinement.
Methods supporting interdisciplinary discovery and cross-domain knowledge exploration.
Applications and use cases demonstrating how ontologies, knowledge graphs, and LLMs support
research tasks, such as literature exploration, hypothesis generation, and knowledge
synthesis.
Assessment
Novel methods, indicators, and metrics for quality and impact assessment of scientific
publications, datasets, software, and other research output.
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.
Approaches to research assessment aligned with responsible research evaluation initiatives
(e.g., DORA, CoAra). .
Metrics and frameworks for evaluating the quality, completeness, and reliability of
scientific knowledge representations, including knowledge graphs and ontologies.
Evaluation of discovery systems and AI-driven tools, including their effectiveness,
transparency, interpretability, and support for research tasks.
Benchmarking and evaluation methodologies for scholarly data infrastructures and AI-based
research support systems (using ontologies, LLMs, KGs).
Keynote Speaker
Dr. Silvio Peroni
Associate Professor at the Department of Classical Philology and Italian Studies, University of Bologna. Director of OpenCitations.
There and Back Again: A Journey about Interoperability, Open Scholarly Infrastructures, and Scientific Knowledge Graphs
Abstract:
While it is difficult to formally define what they are precisely, Open Scholarly Infrastructures (OSIs) are a fundamental building block for the implementation of the Open Science goals as introduced by UNESCO, since they play roles in reforms, address critical gaps, and, more generally, enable more inclusive, equitable, and transparent science in all disciplines and academic communities. Even if the OSI community broadly agrees that the sum of their endeavours could surpass the outreach of proprietary systems, presenting such OSIs as a unique decentralised commodity requires first addressing one of the most crucial and delicate problems in information systems: interoperability. In this talk, I will introduce recent efforts from the past few years to provide standards and tools that support the implementation of interoperability, particularly at the technical and semantic levels, with a focus on the OSIs maintaining Scientific Knowledge Graphs (SKGs), which is one of the main topics of the Sci-K workshop series.
Bio:
I hold a Ph.D. degree in Computer Science, and I am an Associate Professor at the Department of Classical Philology and Italian Studies, University of Bologna, where I teach 'Basic Informatics', 'Introduction to Programming', 'Computational Management of Data' and 'Open Science'.
I am an expert in document markup and semantic descriptions of bibliographic entities using OWL ontologies. I am Director of the Research Centre for Open Scholarly Metadata, one of the main developers of the SPAR (Semantic Publishing and Referencing) Ontologies, Director of OpenCitations, and founding member of the Initiative for Open Citations (I4OC) and the Initiative for Open Abstract (I4OA).
I have been Principal Investigator of the Spoke 4 of the Project CHANGES (Cultural Heritage Active Innovation For Next-Gen Sustainable Society) and of the H2020 project SPICE – Social cohesion, Participation, and Inclusion through Cultural Engagement. Also, I have participated in several EU projects as Local Unit (UNIBO) Coordinator, including LUMEN, GRAPHIA, GraspOS, and OpenAIRE-Nexus. I am an Editorial Board member of Journal of Documentation (Emerald) and PeerJ Computer Science (PeerJ), among others, and I have been involved in the organisation and programme committee of several Semantic Web venues, including ISWC and ESWC.
Among my research interests are Semantic Web technologies, markup languages for complex documents, design patterns for digital documents and ontology modelling, and automatic processes of analysis and segmentation of documents. My works concern theoretical studies and technical implementation of tools to foster semantic interoperability of Open Science services and infrastructures, the empirical analysis of the nature of scholarly citations, bibliometrics and scientometrics studies, visualisation and browsing interfaces for semantic data, and the development of ontologies to manage, integrate and query bibliographic information and cultural heritage data.
Recently, I have become the UNIBO representative for the Barcelona Declaration on Open Research Information (DORI) and a member of several international working groups, including the CoARA Working Group Towards Open Infrastructures for Responsible Research Assessment (OI4RRA) and the RDA Working Group Scientific Knowledge Graphs Interoperability Framework (SKG-IF).
Accepted Papers
ArticleMiner: Ontology-Guided Knowledge Graph Construction from Scientific Publications
Md Abrar Jahin, Craig A. Knoblock and Jay Pujara.
Read paper
From Data Footprints to Researcher Fingerprints: Temporal Ego-centric Research Group Detection in Scientific Knowledge Graphs
Simone Angioni, Miriam Baglioni, Andrea Mannocci and Paolo Manghi.
Read paper
Knowledge Graph Generation from Biodiversity Catalogs with LLM Annotation
Sefika Efeoglu and Jana Hoffmann.
Read paper
Local Citation Recommendation via Heterogeneous Graph Neural Networks and Contrastive Learning
Bahaeddine Hantous, Bruno Yun and Nadia Yacoubi-Ayadi.
Read paper
Open-Schema Metadata Extraction from GitHub READMEs: A Cross-Model Agreement Study
Genet Asefa Gesese, Zongxiong Chen, Sonja Schimmler and Harald Sack.
Read paper
Paper2ModelCard: A framework for the automated generation of model cards from research papers
Mateo Campaya-Pinto, Elvira Amador-Domínguez, Erick Cedeño, Daniel Garijo and Sergio Zaballos-Herrera.
Read paper
ReCitE: Evaluating Citation Context Quality and Relevance with Large Language Models
Ahmar Kamal Hussain, Bernhard Sabel and Andreas Nürnberger.
Read paper
Representing Evidence of Emotional Introspection in Large Language Models: A Scientific Knowledge Graph Research Agenda
Rashid Hmaidan, Antonio Puertas Gallardo, Sergio Consoli, Alessandro Zani, Mario Ceresa and Diego Reforgiato.
Read paper
Research Contributions: Towards a Formal and Semantic Representation
Simone Angioni, Allard Oelen and Angelo Salatino.
Read paper
Retraction without Traction: Towards Handling Post-publication Invalidation in Open Science Infrastructure
Christoph Stade and Dexter Williams.
Read paper
SciGraphChat: Retrieval-Augmented Generation over Scientific Knowledge Graphs
Angelika Kaplan, Jan Keim, Xiyu Zhong and Anne Koziolek.
Read paper
SUDO: From Scientific Publications to Global Scholarly Discourse
Nipun Pathirage, Henrique Santos and Deborah L. McGuinness.
Read paper
Supporting the understanding of ontologies for scientific knowledge graphs with the new version of LODE
Valentina Pasqual, Ahmadreza Nazari and Silvio Peroni.
Read paper
The CIDOC CRM Periodic Table: Documentation You Can Test to Support the Adoption of Scientific Research Ontologies for the Humanities
Remo Grillo.
Read paper
Important Dates
1
Paper submission
July 24th, 2026 August 5th, 2026 (23:59, AoE timezone)
Notification of acceptance
August 21st, 2026 August 25th, 2026
2
3
Camera ready due
September 13th, 2026
Workshop day
October 26th, 2026
4
Submission guidelines
Submissions are welcome in the following categories:
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.
Submissions for review must be in PDF format. They must be self-contained and written in English.
Submissions that do not follow these guidelines, or do not view or print properly, will be rejected
without review.
Sci-K will adopt a single-anonymous review process, and each paper will be reviewed by at least three
Program Committee members.
The proceedings of the workshops will be published on CEUR either as a standalone volume or in companion
proceedings of ISWC 202.
Submit your contributions to Sci-K 2026 Easychair page:
Sci-K 2026 is proud to partner with OpenAIRE to offer a €300 prize for the best paper making
significant use of the OpenAIRE Graph — one of the largest open scholarly knowledge graphs available,
covering millions of publications, datasets, software, projects, and organisations.
Whether you use it as your sole data source or alongside others,
if the OpenAIRE Graph plays a central role in your methodology or findings, your paper is automatically in the running.
The winner will be announced on the workshop day. Don't miss this opportunity to get recognised for doing open science with open data!
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.
Here is what you need to do:
Deadline: Submit by 24 July 2026, 23:59 AoE
Formatting: Convert your paper from the LNCS template to CEUR-WS and shorten it to a
maximum of 12 pages.
Reviews: Attach your ISWC main track reviews in appendix. Including these will
significantly speed up our review process, as we'll only need to assign one reviewer. Your reviews
will not be published.
ISWC2026 will be an in-person conference. All Sci-K papers that will be presented at the workshop
and at
least one author per accepted paper must register to the conference.
More information about registration, venue accomodation, VISA requirements and student grants will
be provided in due course.
Program Committee
PC members in alphabetical order.
Bradley Allen (University of Amsterdam)
Simone Angioni (ISTI CNR)
Arshad Beg (Maynooth University, Ireland.)
Grégoire Burel (The Open University)
Davide Buscaldi (LIPN, Université Paris 13, Sorbonne Paris Cité)
Ezgi Cavus (Middle East Technical University)
Serafeim Chatzopoulos (Athena Research Center)
Auriol Degbelo (Chair of Geoinformatics, TU Dresden)
Giorgio Maria Di Nunzio (University of Padua)
Hang Dong (University of Exeter)
Yuanxi Fu (University of Illinois at Urbana-Champaign)
Sasi Kiran Gaddipati (TIB - Leibniz Information Centre for Science and Technology)
Daniel Garijo (Universidad Politécnica de Madrid)
Genet Asefa Gesese (FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur)
Esteban González Guardia (Ontology Engineering Group - Universidad Politecnica de Madrid)
Md Abrar Jahin (USC Information Sciences Institute)
Mohamad Yaser Jaradeh (L3S Research Center, Leibniz University Hannover)
Alireza Javadian Sabet (University of Pittsburgh)
Lena John (TIB - Leibniz Information Centre for Science and Technology)
Angelika Kaplan (Karlsruhe Institute of Technology)
Jan Keim (Karlsruhe Institute of Technology (KIT))
Mutahira Khalid (TIB - Leibniz Information Centre for Science and Technology)
Soo-Yon Kim (RWTH Aachen University)
Craig Knoblock (USC Information Sciences Institute)
Held in conjunction with the International Semantic Web Conference (ISWC) 2026,
Bari (Italy)
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.
Topics of Interest
We encourage the submission of papers covering, but not limited to, one or more
of
the following topics:
Representation
Data models for the description of scholarly data and their
relationships, including rich semantic representations of hypotheses,
methods, claims, and research results.
Description and use of provenance information of scientific data.
Integration and interoperability models of different data sources,
including cross-domain and interdisciplinary knowledge alignment
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.
Infrastructures and metadata standards aligned with the Barcelona
Declaration to ensure open and sustainable research information.
Applications of representation approaches in scholarly communication,
including semantic publishing and structured scientific communication.
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.
Hybrid human-AI workflows for discovery, including curation, validation,
and knowledge refinement.
Methods supporting interdisciplinary discovery and cross-domain
knowledge exploration.
Applications and use cases demonstrating how ontologies, knowledge
graphs, and LLMs support research tasks, such as literature exploration,
hypothesis generation, and knowledge synthesis.
Assessment
Novel methods, indicators, and metrics for quality and impact assessment
of scientific publications, datasets, software, and other research
output.
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.
Approaches to research assessment aligned with responsible research
evaluation initiatives (e.g., DORA, CoAra). .
Metrics and frameworks for evaluating the quality, completeness, and
reliability of scientific knowledge representations, including knowledge
graphs and ontologies.
Evaluation of discovery systems and AI-driven tools, including their
effectiveness, transparency, interpretability, and support for research
tasks.
Benchmarking and evaluation methodologies for scholarly data
infrastructures and AI-based research support systems (using ontologies,
LLMs, KGs).
Submission Guidelines
Submissions are welcome in the following categories:
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. You can download an offline version
with
the
style
files from http://ceur-ws.org/Vol-XXX/CEURART.zip.
It also contains DOCX template files. Overleaf users may want to use the
CEURART template available in Overleaf. Please adhere also to the
CEUR-WS Policy
on
AI-Assisting Tools.
The proceedings of the workshops will be published on CEUR (indexed in Scopus,
DBLP
and so on.)
Sci-K 2026 is proud to partner with OpenAIRE to offer a €300 prize for the best paper making
significant use of the OpenAIRE Graph — one of the largest open scholarly knowledge graphs available,
covering millions of publications, datasets, software, projects, and organisations.
Whether you use it as your sole data source or alongside others,
if the OpenAIRE Graph plays a central role in your methodology or findings, your paper is automatically in the running.
The winner will be announced on the workshop day. Don't miss this opportunity to get recognised for doing open science with open data!
Allard Oelen, TIB Hannover, 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
