Call for papers: Special Issue on
This special issue follows the 4th International Workshop on Semantic Web and Ontology Design for Cultural Heritage (https://swodch2024.sciencesconf.org/), SWODCH 2024, held on October 30-31, 2024, in Tours,
France.
Starting from the assumption that transdisciplinarity is a key characteristic of research in Digital Cultural Heritage and that knowledge representation models and associated computational techniques are mature enough to provide full-fledged virtual environments
to Humanities for a new era of digitally enabled research and teaching, the aim of the SWODCH workshop series is to create a fruitful dialogue among the communities of ontology designers, knowledge representation specialists, and Semantic Web scholars and
practitioners focusing on digital Cultural Heritage.
Similarly, the scope of the present special issue includes philosophical and social analyses of Cultural Heritage data and knowledge, covering already existing community modelling practices, as well as the historical and social dimensions of data and the explicit
formal representation of these dimensions in a way that is transparent and accessible to both humans and machines. We also welcome studies of principled methodologies and technologies to semantically characterise, integrate, and automatically reason on data
and domain knowledge models. Finally, we invite the submission of contributions discussing recent experiences in developing and deploying Semantic Web solutions to expose, link and access Cultural Heritage data in a harmonised way, and to support the exploitation
of existing semantic models and datasets.
We encourage the authors of papers presented at SWODCH to submit extended versions of their workshop papers (see the “Authors Guidelines” section below for details). We also invite any researcher or practitioner in Digital Cultural Heritage to submit original work related (but not limited) to one or more of the following topic areas:
We invite full papers, dataset descriptions, application reports and reports on tools and systems. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this special
issue. Authors can extend previously published conference or workshop papers; guidelines for this can be found in FAQ 9. Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net.
Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors. We welcome any submission type as described http://www.semantic-web-journal.net/authors#types.
While there is no upper limit, paper length must be justified by content.
Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the “Semantic Web and Ontology Design for Cultural Heritage” Special Issue. All manuscripts will be reviewed based on the SWJ
open and transparent review policy and will be made available online during the review process.
Also note that the Semantic Web journal is open access and all submissions rely on an open and transparent review process (see FAQ 1). Finally please note that submissions must comply with the journal’s Open Science Data requirements, which are detailed in
the corresponding blog post.
For any work using LLM and generative algorithms, we expect an assessment of the energy cost and carbon footprint of the proposed solution, on the one hand, and for the work that went into developing it, on the other. The “Machine Learning Emissions Calculator”
(https://calculator.linkeddata.es/) is a tool made by the Montreal Institute for Learning Algorithms, Element AI and Polytechnique Montreal that can be used to estimate how much carbon is being generated during
training tasks based on several main factors: the energy that is consumed by the system’s hardware; length of training time; the geographical location of the server being used by the provider of cloud computing services; the CO2 emissions per unit of electricity
produced in that particular region; and any potential carbon offsets that have been purchased by the cloud provider.
In the absence of such an evaluation, the article will be desk rejected.
We kindly ask authors to adopt inclusive language in their papers and presentations (https://dbdni.github.io/pages/inclusivewriting.html and https://dbdni.github.io/pages/inclusivetalks.html), and all participants to adopt a proper code on conduct (https://dbdni.github.io/pages/codeofconduct.html).
Antonis Bikakis, University College London, U.K.
Roberta Ferrario, ISTC-CNR, Trento, Italy.
Stéphane Jean, University of Poitiers - ENSMA, France.
Béatrice Markhoff, University François Rabelais de Tours, France.
Alessandro Mosca, ISTC-CNR, Trento, Italy & Free University of Bolzano, Italy.
Marianna Nicolosi Asmundo, University of Catania, Italy.
Trond Aalberg, Norwegian University of Science and Technology, Norway
Valentina Bartalesi, ISTI-CNR, Italy
Carmen Brando, EHESS-CRH, France
Stefano Faralli, Sapienza University of Rome, Italy
Manolis Gergatsoulis, Ionian University, Greece
Kalliopi Kontiza, University College London, UK
Efstratios Kontopoulos,Catalink Ltd, Cyprus
Kostas Kotis, University of the Aegean, Greece
Yannis Marketakis, FORTH, Greece
Ludovica Marinucci, CNR Rome, Italy
Nada Mimouni, Conservatoire National des Arts et Métiers, France
Laura Pandolfo, University of Sassari, Italy
Davide Picca, University of Lausanne, Switzerland
Michalis Sfakakis, Ionian University, Greece
Sofia Stamou, Ionian University, Greece
Maria Rosaria Stufano Melone, Politecnico di Bari, Italy
Maria Theodoridou, FORTH, Greece
Konstantin Todorov, University of Montpellier, France
Christos Tryfonopoulos, University of the Peloponnese, Greece
Douglas Tudhope, University of South Wales, UK
Jouni Tuominen, University of Helsinki, Finland
Valentina Vassallo, The Cyprus Institute, Cyprus
Costas Vassilakis, University of the Peloponnese, Greece
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