The 18th International Workshop on Ontology Matching

Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful technique in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data interlinking, query answering or navigation over knowledge graphs. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate.

Audience:

The workshop encourages participation from academia, industry and user institutions with the emphasis on theoretical and practical aspects of ontology matching. On the one side, we expect representatives from industry and user organizations to present business cases and their requirements for ontology matching. On the other side, we expect academic participants to present their approaches vis-a-vis those requirements. The workshop provides an informal setting for researchers and practitioners from different related initiatives to meet and benefit from each other's work and requirements.

This year, in sync with the main conference, we encourage submissions specifically devoted to: (i) datasets, benchmarks, software tools/services, APIs, methodologies, protocols and metrics (not necessarily related to OAEI), and (ii) application of ontology and instance matching technology in a specific domain and assessment of its usefulness to the final users.

Topics of interest include but are not limited to:

• Business and use cases for matching (e.g., big, open, closed data)
• Requirements to matching from specific application scenarios (e.g., public sector)
• Application of matching techniques in real-world scenarios (e.g., in cloud, with mobile apps)
• Formal foundations and frameworks for matching
• Novel matching methods, including link prediction, ontology-based data access
• Matching and knowledge graphs
• Matching and deep learning
• Matching and embeddings
• Matching and big data
• Matching and linked data
• Instance matching, data interlinking and relations between them
• Privacy-aware matching
• Process model matching
• Large-scale and efficient matching techniques
• Matcher selection, combination and tuning
• User involvement (including both technical and organizational aspects)
• Explanations in matching
• Social and collaborative matching
• Uncertainty in matching
• Expressive alignments
• Reasoning with alignments
• Alignment coherence and debugging
• Alignment management
• Matching for traditional applications (e.g., data science)
• Matching for emerging applications (e.g., web tables, knowledge graphs)

Contributions to the workshop can be made in terms of technical papers and posters/statements of interest addressing different issues of ontology matching as well as participating in the OAEI 2023 campaign. Long technical papers should be of max. 12 pages. Short technical papers should be of max. 6 pages. Posters/statements of interest should not exceed 3 pages. All contributions have to be prepared using the CEUR-ART, 1-column style. Overleaf page for LaTeX users is available at https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/wqyfdgftmcfw, while offline version with the style files is available from http://ceur-ws.org/Vol-XXX/CEURART.zip. Submissoins should be uploaded in PDF format (no later than July 31st, 2023) through the workshop submission site at:

https://www.easychair.org/conferences/?conf=om2023

Long Technical Papers:

• Truveta mapper: a zero-shot ontology alignment framework
  Mariyam Amir, Murchana Baruah, Mahsa Eslamialishah, Sina Ehsani, Alireza Bahramali, Sadra Naddaf-Sh, Saman Zarandioon
• The role of ontology matching in ontology network development
  Sheeba Samuel, Birgitta König-Ries, Alsayed Algergawy
• Matching table metadata with business glossaries using large language models
  Elita Lobo, Oktie Hassanzadeh, Nhan Pham, Nandana Mihindukulasooriya, Dharmashankar Subramanian, Horst Samulowitz
• Contextualized structural self-supervised learning for ontology matching
  Zhu Wang
• Evaluation toolkit for API and RDF alignment
  Tobias Zeimetz, Maurice Büsching, Fabian Birringer, Christoph Otter, Daniel Zeiler, Ralf Schenkel

Organizing Committee:

• Pavel Shvaiko (Main contact)
  Trentino Digitale, Italy
  E-mail: pavel [dot] shvaiko [at] tndigit [dot] it
• Jérôme Euzenat
  INRIA & Univ. Grenoble Alpes, France
  
• Ernesto Jiménez-Ruiz
  City, University of London, UK & SIRIUS, University of Oslo, Norway
• Oktie Hassanzadeh
  IBM Research, USA
• Cássia Trojahn
  IRIT, France

Program Committee:

• Alsayed Algergawy, Jena University, Germany
• Manuel Atencia, Universidad de Málaga, Spain
• Jiaoyan Chen, University of Oxford, UK
• Jérôme David, University Grenoble Alpes & INRIA, France
• Gayo Diallo, University of Bordeaux, France
• Daniel Faria, INESC-ID&IST, University of Lisbon, Portugal
• Alfio Ferrara, University of Milan, Italy
• Marko Gulić, University of Rijeka, Croatia
• Wei Hu, Nanjing University, China
• Ryutaro Ichise, National Institute of Informatics, Japan
• Antoine Isaac, Vrije Universiteit Amsterdam & Europeana, Netherlands
• Naouel Karam, Fraunhofer, Germany
• Prodromos Kolyvakis, EPFL, Switzerland
• Patrick Lambrix, Linköpings Universitet, Sweden
• Oliver Lehmberg, University of Mannheim, Germany
• Fiona McNeill, University of Edinburgh, UK
• Hoa Ngo, CSIRO, Australia
• George Papadakis, University of Athens, Greece
• Catia Pesquita, University of Lisbon, Portugal
• Henry Rosales-Méndez, University of Chile, Chile
• Booma Sowkarthiga, Microsoft, USA
• Kavitha Srinivas, IBM, USA
• Giorgos Stoilos, University of Oxford, UK
• Valentina Tamma, University of Liverpool, UK
• Ludger van Elst, DFKI, Germany
• Xingsi Xue, Fujian University of Technology, China
• Ondřej Zamazal, Prague University of Economics, Czech Republic
• Songmao Zhang, Chinese Academy of Sciences, China
• Lu Zhou, TigerGraph, USA

Acknowledgements: