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Deprecated: Constant E_STRICT is deprecated in /is/htdocs/wp1066709_HN3MCMCRY7/www/swj/includes/errors.inc on line 29 Cogan Shimizu's blog
https://www.semantic-web-journal.net/blog/cogan-shimizu
enSpecial Issue on Semantic Materials Science and Engineering (SMSE)
https://www.semantic-web-journal.net/blog/special-issue-semantic-materials-science-and-engineering-smse
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Call for papers: Special Issue on </p>
<h1>Semantic Materials Science and Engineering (SMSE)</h1>
<p>Materials Science and Engineering (MSE) is currently navigating a profound transformation, often described as the shift toward Industry 5.0 and Materials 4.0. While high-throughput experimentation and multi-scale modeling have generated an unprecedented volume of data, the discipline faces a critical bottleneck: data fragmentation and lack of interoperability. The focus of this special issue lies in the transition from data-driven to knowledge-driven materials science. Current approaches often rely on black-box Machine Learning models that, while powerful, lack explainability and physical context.</p>
<p>Semantic MSE (SMSE) proposes a symbiotic integration where:</p>
<ul>
<li>Semantic Web technologies provide the backbone for interoperability, reasoning, and adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles.</li>
<li>Domain knowledge is formalized via ontologies, ensuring that data retains its physical meaning across scales - from quantum mechanical calculations to macroscopic continuum mechanics.</li>
</ul>
<p>As robotic platforms begin to automate the synthesis and characterization of materials, the need for machine-interpretable communication protocols is no longer theoretical, it is an engineering necessity. Furthermore, the advent of Large Language Models (LLMs) offers new opportunities to populate Knowledge Graphs (KGs) or derive ontologies from unstructured literature, provided these probabilistic models can be grounded in rigorous semantic frameworks to prevent hallucinations.</p>
<p>This special issue aims to address these questions, among others, by seeking new methods, models, and resources that advance research in semantic materials science and engineering.</p>
<p>Relevant topics include, but are not limited to:</p>
<ul>
<li><b>Ontological Architectures for Multiscale Physics:</b> The development and alignment of domain ontologies (e.g., crystallography, defects, thermodynamics) or ontology design patterns with top-level or mid-level frameworks (e.g., BFO, EMMO, PMDco) to support cross-domain interoperability.</li>
<li><b>Neurosymbolic Material Discovery:</b> Methods that combine KGs with Deep Learning to improve , as e.g., property prediction accuracy.</li>
<li><b>Semantic Interoperability in Manufacturing:</b> Utilization of AAS (Asset Administration Shell) and semantic digital twins to map the “Processing-Structure-Property-Performance" (PSPP) interdependencies in real-time industrial environments.</li>
<li><b>Automated Knowledge Extraction:</b> Novel pipelines utilizing Natural Language Processing (NLP) and LLMs to extract structured material data (e.g., synthesis recipes, phase diagrams) and ontologies from unstructured scientific text and patents.</li>
<li><b>Autonomous Neurosymbolic Research Assistants:</b> Agentic systems that combine LLMs with KGs/ontologies to support or automate materials R&D workflows (e.g., hypothesis generation, experiment planning, literature-to-KG curation, and closed-loop optimization), with semantic grounding, provenance tracking, and verification/guardrails to ensure trustworthy outputs.</li>
<li><b>Decentralized Data Marketplaces:</b> Architectures leveraging Linked Data and Solid pods to facilitate the secure, sovereign exchange of proprietary material data between academic and industrial stakeholders.</li>
</ul>
<h2>Submission Guidelines</h2>
<p>Submission deadline: December 16, 2026. Papers submitted before the deadline will be reviewed upon receipt.</p>
<p>Submissions should be made through the Semantic Web Journal website at <a href="http://www.semantic-web-journal.net">http://www.semantic-web-journal.net</a>. Authors must take notice of the submission guidelines posted at <a href="http://www.semantic-web-journal.net/authors">http://www.semantic-web-journal.net/authors</a>.</p>
<p>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 (cf submission guidelines). Please indicate in the cover letter that it is for the Special Issue on “Semantic Materials Science and Engineering (SMSE)." All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.</p>
<p>Also note that the Semantic Web journal is open access and all submissions rely on an open and transparent review process. There is an APC of $2,250. Please see FAQ 3 for more information.</p>
<h2>Guest Editors:</h2>
<ul>
<li>Dr. Ozan Dernek, SDLE Research Center, Case Western Reserve University, Cleveland OH, USA</li>
<li>Dr. Jennifer D’Souza, Data Science & Digital Libraries Research Group, TIB - Leibniz Information Center for Science and Technology, Hannover, Germany</li>
<li>Dr. Huanyu Li, Department of Computer and Information Science, Linköping University, Linköping, Sweden</li>
<li>Prof. Roger H. French, Department of Materials Science and Engineering, Case Western Reserve University, Cleveland OH, USA</li>
<li>Prof. Patrick Lambrix, Department of Computer and Information Science, Linköping University, Sweden</li>
<li>Prof. Harald Sack, Department of Information Service Engineering, FIZ Karlsruhe - Leibniz Institute for Information Infrastructure & Karlsruhe Institute of Technology, Karlsruhe, Germany</li>
</ul>
<h2>Guest Editorial Board</h2>
<ul>
<li>Toshihiro Ashino, Toyo University & NIMS, Japan</li>
<li>Bernd Bayerlein, BAM, Germany</li>
<li>Owain Beynon, University College London, UK </li>
<li>Hossein Beygi Nasrabadi, FIZ Karlsruhe, Germany</li>
<li>Diego Collorana, Fraunhofer FIT, Germany</li>
<li>Martin Thomas Horsch, Norwegian University of Life Sciences, Norway</li>
<li>Emanuele Ghedini, University of Bologna, Italy</li>
<li>Gerhard Goldbeck, Goldbeck Consulting, UK</li>
<li>Abril Azocar Guzman, Forschungszentrum Jülich, Germany</li>
<li>Adham Hashibon, University College London, UK</li>
<li>Jesper Friis, SINTEF, Norway</li>
<li>Philipp von Hartrott, Fraunhofer IWM, Germany</li>
<li>Lars Vogt, Leibniz IABC, Germany</li>
<li>Markus Schilling, BAM, Germany</li>
<li>Jörg Waitelonis, FIZ Karlsruhe, Germany</li>
<li>He Tan, Jönköping University, Sweden</li>
<li>Patrik Schneider, Siemens AG, Germany & TU Wien, Vienna, Austria</li>
<li>Anmol Sain, Wright State University, USAi</li>
</ul>
</div></div></div>Tue, 03 Mar 2026 19:34:45 +0000Cogan Shimizu4035 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/special-issue-semantic-materials-science-and-engineering-smse#commentsSpecial Issue on Geospatial Knowledge Graphs
https://www.semantic-web-journal.net/blog/special-issue-geospatial-knowledge-graphs
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Call for papers: Special Issue on </p>
<h1>Geospatial Knowledge Graphs </h1>
<p>Knowledge graphs (KGs) are rooted in Semantic Web technologies, an extension of the World Wide Web that allows data to be published in a model that is both human-readable and machine-interpretable. Since space and place often serve as key nexuses in knowledge graphs—linking diverse entities such as people, organizations, events, and more—Geospatial Knowledge Graphs (GeoKGs) have emerged. GeoKGs emphasize the role of spatial and temporal dimensions within the KGs, supporting geospatial data organization, retrieval, and synthesis. Notable GeoKGs include YAGO2, LinkedGeoData, GeoNames, GNIS-LD, KnowWhereGraph, among others. With the advancement of AI and Geospatial AI technologies, GeoKGs have become a promising toolset for organizing and synthesizing geospatial data into structured formats, enabling various applications like Geospatial Data Integration, Geoparsing, Geographic Information Retrieval, and Geographic Question Answering.</p>
<p>In recent years, the rise of large foundation models (LFMs)—such as GPT-4, LLAVA, Gemini—and retrieval-augmented generation (RAG) technologies has underscored the growing importance of knowledge graphs in cutting-edge AI applications, especially following the release of Microsoft's GraphRAG package. This raises several intriguing research questions: How can we develop KGs and GeoKGs that are better suited for current LFM and RAG-based applications? How can GeoKGs, particularly their geospatial information, be leveraged to inform LFMs for fairer, more accurate, and up-to-date predictions? How the LFMs can be used to enhance the development of GeoKGs?</p>
<p>This special issue aims to address these questions, among others, by seeking new methods, models, and resources that advance research in Geospatial Knowledge Graphs and GeoAI. Relevant topics include, but are not limited to:</p>
<ul>
<li><strong>Geospatial Knowledge Graph Construction</strong></li>
<ul>
<li>Geo-Ontology Engineering</li>
<li>Geographic Ontology Alignment</li>
<li>Geographic Entity Similarity Measurement (Alignment)</li>
<li>Automatically GeoKG Construction from Unstructured Text</li>
<li>Coreference Resolution for Geographic Entities</li>
<li>Spatio-Temporal Scoping of Knowledge Graphs</li>
</ul>
<li><strong>Geospatial Knowledge Graph Management</strong></li>
<ul>
<li>Geospatial Knowledge Graph Completion</li>
<li>GeoSPARQL and Spatial Query Evaluation</li>
<li>Federated Query across Geospatial Knowledge Graphs</li>
<li>GeoKG-based Geovisualization and Decision Support</li>
<li>Virtual GeoKGs</li>
</ul>
<li><strong>Query Geospatial Knowledge Graphs</strong></li>
<ul>
<li>GeoSPARQL and spatial query evaluation</li>
<li>Indexing geospatial knowledge graphs</li>
<li>Spatial query benchmark</li>
</ul>
<li><strong>Downstream Applications on Geospatial Knowledge Graphs</strong></li>
<ul>
<li>Geographic Question Answering and Semantic Parsing based on Knowledge Graphs</li>
<li>Geospatial Knowledge Graph Summarization</li>
<li>Geospatial Data Integration</li>
<li>Geographic Information Retrieval</li>
<li>Geospatial Visualization</li>
</ul>
<li><strong>Geo-Text Analysis</strong></li>
<ul>
<li>Geospatial Information Extraction</li>
<li>Toponym Recognition and Toponym Resolution</li>
<li>Geospatial-Aware Language Model Development</li>
</ul>
<li><strong>Deep Learning on Geospatial Knowledge Graphs</strong></li>
<ul>
<li>Geographic Knowledge Graph Embeddings</li>
<li>Spatiotemporally explicit machine learning on KGs and GeoKGs</li>
<li>GraphRAG on GeoKGs</li>
<li>GeoKG development for/with LFM and RAG</li>
</ul>
</ul>
<h2>Submission Guideline</h2>
<p>Submission deadline: May 16, 2025. Papers submitted before the deadline will be reviewed upon receipt.</p>
<h2>Author Guidelines</h2>
<p>Submissions should be made through the Semantic Web Journal website at <a href="http://www.semantic-web-journal.net">http://www.semantic-web-journal.net</a>.<br />
Authors must take notice of the submission guidelines posted at <a href="http://www.semantic-web-journal.net/authors">http://www.semantic-web-journal.net/authors</a>.</p>
<h2>Guest Editors:</h2>
<ul>
<li>Prof. <a href="https://gengchenmai.github.io/">Gengchen Mai</a>, Department of Geography and the Environment, University of Texas at Austin</li>
<li>Dr. <a href="https://scholar.google.se/citations?user=gDM3WGwAAAAJ&hl=en">Weiming Huang</a>, School of Geography, University of Leeds</li>
<li>Dr. <a href="https://jinmengrao.com/">Jinmeng Rao</a>, Google DeepMind</li>
<li>Prof. <a href="https://ruizhugeographer.com/">Rui Zhu</a>, School of Geographical Sciences, University of Bristol</li>
<li>Ms. <a href="https://meilinshi.github.io/">Meilin Shi</a>, Department of Geography and Regional Research, University of Vienna</li>
</ul>
</div></div></div>Wed, 20 Nov 2024 22:45:45 +0000Cogan Shimizu3783 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/special-issue-geospatial-knowledge-graphs#commentsSpecial issue on Large Language Models, Generative AI and Knowledge Graphs
https://www.semantic-web-journal.net/blog/special-issue-large-language-models-generative-ai-and-knowledge-graphs
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Call for papers: Special Issue on</p>
<h1>Large Language Models, Generative AI and Knowledge Graphs</h1>
<p>The development and deployment of Large Language Models and Generative AI are having a major impact on the Semantic Web: recently, the 21st Extended Semantic Web Conference (ESWC 2024) and the 23rd International Semantic Web Conference (ISWC 2024) have featured Special Tracks on Large Language Models for Knowledge Engineering (<a href="https://2024.eswc-conferences.org/special-track-llms-for-ke/">https://2024.eswc-conferences.org/special-track-llms-for-ke/</a>) and on Harmonising Generative AI and Semantic Web Technologies: Opportunities, challenges, and benchmarks (<a href="https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52/websitePage:98003e12-5a47-49fc-a28b-611610115059">https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589...</a>). An upcoming AAAI Fall Symposium will be specifically focused on Large Language Models for Knowledge Graphs and Ontology Engineering (<a href="https://kastle-lab.github.io/llms-and-kg-engineering/">https://kastle-lab.github.io/llms-and-kg-engineering/</a>). </p>
<p>These events have shown that these models are transforming the ways in which we think about and approach various phases of knowledge graph development, such as requirements engineering, competency questions, ontology engineering, ontology and knowledge graph documentation, etc. At the same time, these models pose new questions and challenges around explainability, accountability and evaluation.</p>
<p>This special issue builds directly on top of the outcomes of these recent events. The scope of the present special issue is to provide an opportunity to develop and expand early results in the area of large language models, generative AI and knowledge graphs previously published in ESWC 2024 and ISWC 2024, as well as to welcome new research in this exciting new area.</p>
<h2>Themes and Topics</h2>
<p>We encourage the authors of papers presented at the Special Track on LLMs for KE in ESWC 2024 and the Special Session on LLMs at ISWC 2024 to submit extended versions of their papers (see the “Author Guidelines” section below for details). We also encourage the submission of novel, previously unpublished research related, but not limited, to one or more of the following themes and topics:</p>
<ul>
<li>Techniques for seamless integration and interoperability between KGs, LLMs, and other AI components</li>
<li>Novel methodologies for integrating KGs, LLMs, and AI technologies in specific domains, such as natural language processing, information retrieval, decision support systems, etc.</li>
<li>Evaluation of LLMs and Generative AI for Knowledge Engineering</li>
<li>Benchmarks and reproducibility of LLMs/GenAI for Knowledge Engineering</li>
<li>Evaluation of knowledge graph development tasks based on LLMs/GenAI</li>
<li>Knowledge Graph-based retrieval augmented generation (RAG)</li>
<li>Drafting of ontology classes and properties</li>
<li>Knowledge base population and refinement</li>
<li>Knowledge graph completion, including multiple modalities, with LLMs/GenAI</li>
<li>Competency question generation and retrofitting</li>
<li>Human in the loop activities</li>
<li>Data quality and profiling in integrated LLM, GenAI and KG systems</li>
<li>Knowledge base alignment</li>
<li>Extraction of rich knowledge structures</li>
<li>Generating and extending documentation for ontologies and knowledge graphs</li>
<li>Conversational knowledge engineering</li>
<li>Ontology and knowledge graph engineering methodologies based on LLMs/GenAI</li>
<li>Ontology matching with LLMs/GenAI</li>
<li>Guardrailing, safety and hallucination prevention in using LLMs/GenAI for knowledge graphs</li>
<li>Using LLMs/GenAI in knowledge graph reasoning</li>
</ul>
<h2>Authors Guidelines</h2>
<p>We invite full papers, dataset descriptions, benchmarks, and frameworks 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. In particular, we welcome submissions in the following three strands:</p>
<ol>
<li>ESWC 2024: accepted papers for publication in: (a) the Research Track that used LLMs/GenAI as a principal component of the research; or (b) in the Special Track on LLMs for KE</li>
<li>ISWC 2024: white papers compiling the results and discussions of papers in the Special Session in LLMs</li>
<li>Open call: new research that has previously not been submitted elsewhere</li>
</ol>
<p>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 <a href="http://www.semantic-web-journal.net">http://www.semantic-web-journal.net</a>. Prospective authors must take notice of the submission guidelines posted at <a href="http://www.semantic-web-journal.net/authors">http://www.semantic-web-journal.net/authors</a>. We welcome any submission type as described <a href="http://www.semantic-web-journal.net/authors#types">http://www.semantic-web-journal.net/authors#types</a>. While there is a soft limit of 25 pages, paper length must be justified by content.<br />
Please indicate in the cover letter that it is for the Special Issue on “Large Language Models, Generative AI and Knowledge Graphs." All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.<br />
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.</p>
<h2>Deadline</h2>
<p>Submission deadline: 30 April 2025. Papers submitted before the deadline will be reviewed upon receipt.</p>
<h2>Guest Editors</h2>
<p>Oscar Corcho, Universidad Politécnica de Madrid, Spain<br />
Daniel Garijo, Universidad Politécnica de Madrid, Spain<br />
Paul Groth, University of Amsterdam, NL<br />
Albert Meroño, King’s College London, UK<br />
Elena Simperl, King’s College London, UK<br />
Blerina Spahiu, Università degli Studi di Milano-Bicocca, IT<br />
Valentina Tamma, University of Liverpool, UK<br />
Raphaël Troncy, EURECOM, FR</p>
<h2>Guest Editorial Board</h2>
<p>Bradley Allen, University of Amsterdam<br />
Valentina Anita Carriero, Cefriel, Milan<br />
David Chaves-Fraga, Universidade de Santiago de Compostela<br />
Diego Collarana, Enterprise Information System (EIS)<br />
Jennifer D'Souza, TIB Leibniz Information Centre for Science and Technology University Library<br />
Mauro Dragoni, Fondazione Bruno Kessler - FBK-IRST<br />
Fajar J. Ekaputra, Vienna University of Economics and Business (WU)<br />
Catherine Faron, Université Côte d'Azur<br />
Jose Manuel Gomez-Perez, expert.ai<br />
Peter Haase, metaphacts<br />
Nitisha Jain, King's College London, London, United Kingdom<br />
Jan-Christoph Kalo, University of Amsterdam<br />
Vamsi Krishna Kommineni, Friedrich Schiller University Jena<br />
Birgitta König-Ries, Friedrich Schiller University of Jena<br />
Anna Sofia Lippolis, CNR ISTC<br />
Pasquale Lisena, EURECOM<br />
Andrea Giovanni Nuzzolese, University of Bologna<br />
Bernardo Pereira Nunes, Australian National University<br />
Rafael Peñaloza, University of Milano-Bicocca<br />
Gerard Pons, Universitat Politècnica de Catalunya<br />
Célian Ringwald, Université Côte d’Azur, Inria, CNRS, I3S<br />
Catherine Roussey, INRAE<br />
Anisa Rula, University of Brescia<br />
Marta Sabou, Vienna University of Economics and Business<br />
Sanaz Saki Norouzi, Kansas State University<br />
Sheeba Samuel, Friedrich Schiller University, Jena<br />
Chris Davis Jaldi, Wright State University<br />
Angelo Salatino, Open University, UK<br />
Emanuele Laurenzi, University of Applied Sciences and Arts Northwestern Switzerland</p>
</div></div></div>Mon, 30 Sep 2024 23:03:07 +0000Cogan Shimizu3766 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/special-issue-large-language-models-generative-ai-and-knowledge-graphs#comments2024 Journal Impact Metrics
https://www.semantic-web-journal.net/blog/2024-journal-impact-metrics
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>The 2024impact factors were just released, and the Semantic Web journal is at 3.0 - down a bit from 3.105 <a href="https://www.semantic-web-journal.net/blog/2023-journal-impact-metrics">last year</a>. We will compare it with the impact factors from the same journals we compared it with in <a href="https://www.semantic-web-journal.net/blog/2023-journal-impact-metrics">previous years</a>.</p>
<p>First the list of "Web" journals which we generated <a href="http://www.semantic-web-journal.net/blog/semantic-web-journal-impact-factor">seven years ago</a> for comparison, together with the new, updated, impact factors (previous in parentheses).</p>
<p>Semantic Web 3.0 (3.0)<br />
ACM Transactions on the Web 2.6 (3.5)<br />
International Journal on Semantic Web and Information Systems 4.1 (3.2)<br />
International Journal of Web and Grid Services 0.8 (1.0)<br />
International Journal of Web Services Research 1.0 (1.1)<br />
Journal of Web Semantics 2.1 (2.5)<br />
World Wide Web - Internet and Web Information Systems 2.7 (3.7)</p>
<p>Like <a href="https://www.semantic-web-journal.net/blog/2023-journal-impact-metrics">last year</a>, here are some journals (the same as last year in fact) with overlapping scope.</p>
<p>IEEE Transactions on Knowledge and Data Engineering: 8.9 (8.9)<br />
VLDB Journal 2.8 (4.2)<br />
ACM Transactions on Computer-Human Interaction 4.8 (3.7)<br />
Information Systems 3.0 (3.7)<br />
Multimedia Tools and Applications 3.0 (3.6)<br />
Knowledge and Information Systems 2.5 (2.7)<br />
Data & Knowledge Engineering 2.7 (2.5)<br />
Applied Ontology 2.5 (1.0)</p>
<p>Some journals covering generically larger communities of course rather high impact factors.</p>
<p>Communications of the ACM 11.2 (22.7)<br />
Artificial Intelligence 5.1 (14.4)<br />
Knowledge-based Systems 7.2 (8.8)<br />
IEEE Intelligent Systems 5.6 (6.4)</p>
<p>Also, <a href="https://scholar.google.com/">Google Scholar</a> just updated its conference and journal metrics. We can look at the listing of top ranked conferences and journals - in terms of Google Scholar h5-index - with "Web" in the title, via the <a href="https://scholar.google.com/citations?hl=en&view_op=search_venues&vq=web&btnG=">live link</a>. A blogpost on the journal IF has been released by Clarivate <a href="https://clarivate.com/blog/journal-citation-reports-2024-simplifying-journal-evaluation">here</a>.</p>
<p>While this is of course a satisfying ranking for the Semantic Web journal, we want to emphasize that the Google Scholar h5-ranking and the impact factor have to be interpreted with caution, like all such metrics.</p>
</div></div></div>Wed, 18 Sep 2024 13:16:03 +0000Cogan Shimizu3762 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/2024-journal-impact-metrics#commentsSpecial issue on the Semantic Web and Ontology Design for Cultural Heritage
https://www.semantic-web-journal.net/blog/special-issue-semantic-web-and-ontology-design-cultural-heritage
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Call for papers: Special Issue on </p>
<h1>Semantic Web and Ontology Design for Cultural Heritage</h1>
<p>This special issue follows the 4th International Workshop on Semantic Web and Ontology Design for Cultural Heritage (<a href="https://swodch2024.sciencesconf.org/">https://swodch2024.sciencesconf.org/</a>), SWODCH 2024, held on October 30-31, 2024, in Tours, France.<br />
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.<br />
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.</p>
<h2>Themes and Topics</h2>
<p>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:</p>
<ul>
<li> Conceptual analysis and ontology design for the Digital Humanities:
<ul>
<li> Domain ontologies or conceptual models for different fields of Humanities.
</li><li> Methodological aspects of ontology development for the DH.
</li><li> Application of formal ontology theories in the DH.
</li><li> Case studies and lessons learnt from the use of standard ontologies (e.g. CIDOC-CRM).
</li><li> Logical and ontological analysis of CIDOC-CRM or FRBR, e.g., with respect to foundational ontologies (DOLCE, UFO, BFO, etc.).
</li><li> Philosophical and sociological analysis of digital models and modelling practices in DH.
</li><li> Social studies on the policies towards the standardisation of ontologies in DH.
</li></ul>
</li><li> Semantic Web Technologies and Applications for Cultural Heritage:
<ul>
<li> SW technologies for CH content creation, annotation and extraction.
</li><li> SW architectures and infrastructures for CH.
</li><li> Interoperability of CH collections.
</li><li> Applying the FAIR data principles to CH data.
</li><li> CH knowledge graphs.
</li><li> Searching, querying and visualising CH data on the SW.
</li><li> Ontology-based access to CH data.
</li><li> Publishing CH data on the Web.
</li><li> Navigating through and browsing CH data on the Web.
</li><li> SW solutions for trust and provenance issues in CH.
</li><li> SW applications for digital libraries, museums, tourism, the creative industries, etc.
</li></ul>
</li></ul>
<h2>Authors Guidelines</h2>
<p>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 <a href="http://www.semantic-web-journal.net">http://www.semantic-web-journal.net</a>. Prospective authors must take notice of the submission guidelines posted at <a href="http://www.semantic-web-journal.net/authors">http://www.semantic-web-journal.net/authors</a>. We welcome any submission type as described <a href="http://www.semantic-web-journal.net/authors#types">http://www.semantic-web-journal.net/authors#types</a>. While there is no upper limit, paper length must be justified by content.<br />
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.<br />
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.</p>
<h3>Generative Artificial Intelligence carbon footprint.</h3>
<p>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” (<a href="https://calculator.linkeddata.es/">https://calculator.linkeddata.es/</a>) 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.<br />
In the absence of such an evaluation, the article will be desk rejected.</p>
<h3>Diversity and inclusion statement.</h3>
<p>We kindly ask authors to adopt inclusive language in their papers and presentations (<a href="https://dbdni.github.io/pages/inclusivewriting.html">https://dbdni.github.io/pages/inclusivewriting.html</a> and <a href="https://dbdni.github.io/pages/inclusivetalks.html">https://dbdni.github.io/pages/inclusivetalks.html</a>), and all participants to adopt a proper code on conduct (<a href="https://dbdni.github.io/pages/codeofconduct.html">https://dbdni.github.io/pages/codeofconduct.html</a>).</p>
<h2>Deadline</h2>
<ul>
<li> Manuscript submission deadline: June 15, 2025
</li><li> Manuscript review feedback: June 30, 2025
</li></ul>
<h2>Guest Editors</h2>
<p>Antonis Bikakis, University College London, U.K.<br />
Roberta Ferrario, ISTC-CNR, Trento, Italy.<br />
Stéphane Jean, University of Poitiers - ENSMA, France.<br />
Béatrice Markhoff, University François Rabelais de Tours, France.<br />
Alessandro Mosca, ISTC-CNR, Trento, Italy & Free University of Bolzano, Italy.<br />
Marianna Nicolosi Asmundo, University of Catania, Italy.</p>
<h2>Guest Editorial Board</h2>
<p>Trond Aalberg, Norwegian University of Science and Technology, Norway<br />
Valentina Bartalesi, ISTI-CNR, Italy<br />
Carmen Brando, EHESS-CRH, France<br />
Stefano Faralli, Sapienza University of Rome, Italy<br />
Manolis Gergatsoulis, Ionian University, Greece<br />
Kalliopi Kontiza, University College London, UK<br />
Efstratios Kontopoulos,Catalink Ltd, Cyprus<br />
Kostas Kotis, University of the Aegean, Greece<br />
Yannis Marketakis, FORTH, Greece<br />
Ludovica Marinucci, CNR Rome, Italy<br />
Nada Mimouni, Conservatoire National des Arts et Métiers, France<br />
Laura Pandolfo, University of Sassari, Italy<br />
Davide Picca, University of Lausanne, Switzerland<br />
Michalis Sfakakis, Ionian University, Greece<br />
Sofia Stamou, Ionian University, Greece<br />
Maria Rosaria Stufano Melone, Politecnico di Bari, Italy<br />
Maria Theodoridou, FORTH, Greece<br />
Konstantin Todorov, University of Montpellier, France<br />
Christos Tryfonopoulos, University of the Peloponnese, Greece<br />
Douglas Tudhope, University of South Wales, UK<br />
Jouni Tuominen, University of Helsinki, Finland<br />
Valentina Vassallo, The Cyprus Institute, Cyprus<br />
Costas Vassilakis, University of the Peloponnese, Greece</p>
</div></div></div>Wed, 11 Sep 2024 13:11:00 +0000Cogan Shimizu3758 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/special-issue-semantic-web-and-ontology-design-cultural-heritage#commentsSpecial Issue on the Pedagogy and Praxis of Knowledge Graphs and the Semantic Web
https://www.semantic-web-journal.net/blog/special-issue-pedagogy-and-praxis-knowledge-graphs-and-semantic-web
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Call for papers: Special Issue on </p>
<h1>The Pedagogy and Praxis of Knowledge Graphs and the Semantic Web</h1>
<p>The development, deployment, and usage of knowledge graphs (KGs) continues to grow in prevalence and importance, as they enable transformative and convergent research. However, to most effectively ensure the continued growth and current and future usability of a transdisciplinary KG ecosystem, the provision of -- and access to -- comprehensive education, training, and awareness pertaining to the capabilities, resources, limitations, and requirements of the gestalt and constituent techniques and technologies is necessary. Indeed, education is fundamental to nearly every aspect of the knowledge graph ecosystem and, furthermore, every role which will support its construction, usage, maintainability, and sustainability. </p>
<p>Similarly, as the importance, growth, and integration of AI (especially large language models) in many tasks, such as programming assistants or co-pilots, we wish to see similar capability for knowledge engineering tasks. Thus, large corpora of examples of knowledge engineering artifacts (e.g., ontologies developed by students in different stages of their academic preparation) become important to use during training -- or to protect so that they do not become memorized!</p>
<p>Conversely, knowledge graphs (and in particular their schemas, especially if implemented as ontologies) are excellent ways of modeling a domain, and connecting across heterogeneous data. This means that a knowledge graph, in a similar manner to a concept map, can be exceptionally useful as a teaching tool. Indeed, the interconnected articles on Wikipedia constitute one form of a knowledge graph. Knowledge graphs, or other semantic resources, can be effective mechanisms for conveying how different knowledge is connected, and thus demonstrate to students </p>
<p>For this special issue, we invite submissions which advance our ability to instruct, educate, and inspire a new generation of knowledge graph enthusiasts, engineers, and professionals across a wide variety of roles (e.g., from stakeholders to implementers to practitioners). Additionally, we invite submissions pertaining to the use of knowledge graphs in educational contexts. Finally, we also wish to collect descriptions of pedagogical outcomes from courses, summer schools, etc. to assess efficacy of instruction, but also for use in the training and construction of neurosymbolic systems.</p>
<h2>Guest Editors</h2>
<p>Guest Editors can be collectively addressed at edugate @ wright.edu</p>
<p>Cogan Shimizu, Assistant Professor, Wright State University<br />
Pascal Hitzler, Professor, Kansas State University<br />
Florence D. Hudson, Executive Director, Northeast Big Data Innovation Hub<br />
Hande Küçük McGinty, Assistant Professor, Kansas State University<br />
Francois Scharffe, Co-Founder, Knowledge Graph Conference</p>
<h2>Guest Editorial Board</h2>
<p>Timm Capstein<br />
Catherine Faron<br />
Filip Ilievski<br />
Ora Lassila<br />
Agnieszka Ławrynowicz<br />
Ryan McGranaghan<br />
Maria Poveda<br />
Nicole Vasilevsky</p>
<h2>Themes, Topics, and Submission Modes</h2>
<ul>
<li>Education Research – these submissions should examine the principled execution of education research and be submitted as full papers.
<ul>
<li> Example: formal evaluation of KG educational material</li>
<li> Example: formal evaluation of use of KGs as educational material</li>
</ul>
</li><li>Descriptions of Open Source KG/SW Educational Material – these submissions should follow the Dataset Descriptions or Descriptions of Ontologies submission criteria of the SWJ, where appropriate. The Open Source Educational Materials must be made publicly available and obviously and permissibly licensed.
<ul>
<li> Example: A lecture series on fundamental KG education</li>
<li> Example: A KG which supports education in a domain</li>
<li> Example: An Ontology of Educational Practices</li>
</ul>
</li><li>Reports on and Applications of OS KG/SW Educational Material & Tools – these submissions should follow Application reports or Reports on tools and systems submission criteria, as appropriate.
<ul>
<li> Example: A Case Study (with significant discussion and lessons learned) on Using an Educational KG in Class</li>
</ul>
</li><li>Surveys – however, we do not wish to have overlapping surveys, and any prospective authors should first contact the Guest Editors before embarking on their work.
</li></ul>
<h2>Deadline</h2>
<ul>
<li>
Submission deadline: 30th September 2024. Papers submitted before the deadline will be reviewed upon receipt.
</li>
</ul>
</div></div></div>Thu, 29 Feb 2024 04:01:35 +0000Cogan Shimizu3653 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/special-issue-pedagogy-and-praxis-knowledge-graphs-and-semantic-web#commentsSpecial issue on Knowledge Graph Construction
https://www.semantic-web-journal.net/blog/special-issue-knowledge-graph-construction
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>Call for papers: Special Issue on </p>
<h1>Knowledge Graph Construction</h1>
<p>This special issue aims to explore the latest research trends and enhance the state of the art in the field of knowledge graph construction from semi-structured data. While many methods and systems were proposed to construct knowledge graphs from existing semi-structured data stored in databases or available on the Web, knowledge graph construction is a complex task and there are still many open research questions.</p>
<p>Knowledge graphs (KGs) are key to the adoption of the Semantic Web. This special issue should provide a comprehensive overview of the latest research trend and inspire further research in this exciting and rapidly evolving area. We welcome original research papers that propose novel techniques, models, and frameworks for generating high-quality and reliable knowledge graphs from structured and semi-structured data, such as relational databases, CSV, XML, JSON etc. </p>
<p>Meeting the challenges associated with constructing knowledge graphs necessitates the investigation of theoretical concepts and the design of efficient systems and methodologies. Many declarative approaches were proposed, yet they lack corresponding formalizations. Although these declarative methods initially sparked the design of virtualisation systems,they did not gain as much traction as well in the case of heterogeneous data where a multitude of materialisation systems emerged to efficiently construct knowledge graphs. Even among materialisation systems, there is still ample space for enhancements and improvements but systematic evaluation methods were not proposed yet. </p>
<p>Knowledge graph construction was seen as the responsibility of a single entity (typically the data owner). However, with the advent of data spaces, knowledge graph construction has evolved entailing a whole new range of challenges, such as collaborative knowledge graph construction or conformance to a commonly agreed model. While early approaches have predominantly focused on declarative methods and systems to support users with the declaration of rules for knowledge graph construction, there has been lately a growing interest in leveraging large language models (LLMs) and machine learning (ML) algorithms in general in the field, opening up new research avenues for constructing knowledge graphs. Naturally, when machine learning comes into play, additional considerations, such as explainability, reproducibility and replicability become relevant. </p>
<p>Contributions along the aforementioned (but not limited to) directions are considered for the proposed special issue on knowledge graph construction, including theoretical investigations, lessons learned, and observations from in-use experiences.</p>
<h2>Themes and Topics</h2>
<ul>
<li> Automated KG construction
<ul>
<li> (Semi-)automation KG construction
</li><li> Machine Learning algorithms for KG construction, repair and refinement,<br />
e.g., embeddings or Large Language Models (LLM) for KG construction
</li><li> (Symbolic and subsymbolic) Learning for KG construction
</li></ul>
</li><li> Mapping languages
<ul>
<li> Declarative approaches to KG construction
</li><li> Mapping languages for KG construction
</li><li> Formalizations of KG construction
</li><li> RDF and property graphs construction
</li></ul>
</li><li> Governance
<ul>
<li> Data governance and KG Lifecycle management
</li><li> End-to-end architectures and workflows for KG construction
</li><li> Knowledge graph construction within Data Spaces, e.g., IDS, GAIA-X, Solid, etc.
</li></ul>
</li><li> Quality
<ul>
<li> Explainable KG construction
</li><li> Quality methods and systems for KG construction
</li><li> (Semi-)automated validation and repair for KG construction
</li></ul>
</li><li> Virtualization
<ul>
<li> Virtualization over heterogeneous data sources
</li><li> Federation over heterogeneous data sources
</li><li> Hybrid materialization and virtualization over heterogeneous data
</li></ul>
</li><li> Benchmarks
<ul>
<li> Lessons learnt, in-use, and experiences on construction KG in real environments
</li><li> Benchmarks for KG construction for efficiency, scalability, etc.
</li></ul>
</li><li> Human Interaction
<ul>
<li> User interfaces for KG construction
</li><li> Human-in-the-loop KG construction
</li></ul>
</li></ul>
<h2>Relevant Types of Submissions</h2>
<p>The following are relevant types of submissions:</p>
<ul>
<li> Research articles related to the relevant topics (full paper)
</li><li> Surveys on topics related to KG construction (full paper)
</li><li> Ontologies for KG construction (short ontology paper of 10 pages)
</li><li> Application reports with deployed applications for KG construction (short<br />
papers of 10 pages)
</li><li> Systems, UIs or benchmarks for KG construction (short system papers of 10<br />
pages)
</li></ul>
<h2>Deadline</h2>
<ul>
<li>
Submission deadline: 31st Aug 2024. Papers submitted before the deadline will be reviewed upon receipt.
</li>
</ul>
<h2>Guest Editors</h2>
<p>The guest editors can be reached at <a href="mailto:kgc_swj@grupos.nube.usc.gal">kgc_swj@grupos.nube.usc.gal</a>.</p>
<p>David Chaves-Fraga, University of Santiago de Compostela, Spain & KULeuven, Belgium<br />
Christophe Debruyne, University of Liège, Belgium<br />
Anastasia Dimou, KU Leuven, Belgium<br />
Maria-Esther Vidal, Leibniz University of Hannover, and TIB-Leibniz Information Centre for<br />
Science and Technology, Germany</p>
<h2>Guest Editorial Board</h2>
<p>TBA</p>
</div></div></div>Fri, 22 Dec 2023 16:19:49 +0000Cogan Shimizu3604 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/special-issue-knowledge-graph-construction#commentsSemantic Web journal 10-year award 2023
https://www.semantic-web-journal.net/blog/semantic-web-journal-10-year-award-2023
<div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p>We congratulate</p>
<p>Hajo Rijgersberg, Wageningen University & Research<br />
Mark van Assem, NWO<br />
Jan Top, Wageningen University & Research and Vrije Universiteit Amsterdam (retired)</p>
<p>for receiving the</p>
<p><b>Semantic Web journal 10-year award 2023</b></p>
<p>for their paper</p>
<p><a href="https://www.semantic-web-journal.net/content/ontology-units-measure-and-related-concepts">Hajo Rijgersberg, Mark van Assem, Jan Top, Ontology of Units of Measure and Related Concepts. Semantic Web 4 (1), 2013, 3-13.</a></p>
<p>The paper was selected in a vote by all Editorial Board members from the pool of papers published in 2013 by the journal.</p>
<p>The authors will receive as a prize, sponsored by our publisher <a href="https://www.iospress.com/catalog/journals/semantic-web">IOS Press</a>, a book of their choice from the <a href="https://www.iospress.com/catalog/book-series/studies-on-the-semantic-web">Studies on the Semantic Web</a> book series.</p>
</div></div></div>Fri, 10 Nov 2023 15:37:47 +0000Cogan Shimizu3576 at https://www.semantic-web-journal.nethttps://www.semantic-web-journal.net/blog/semantic-web-journal-10-year-award-2023#comments Deprecated: Constant E_STRICT is deprecated in /is/htdocs/wp1066709_HN3MCMCRY7/www/swj/includes/errors.inc on line 29
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