The 17th 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 2022 campaign. Long technical papers should be of max. 12 pages. Short technical papers should be of max. 5 pages. Posters/statements of interest should not exceed 2 pages. All contributions have to be prepared using the LNCS Style and should be submitted in PDF format (no later than August 9th, 2022) through the workshop submission site at:

https://www.easychair.org/conferences/?conf=om2022

Contributors to the OAEI 2022 campaign have to follow the campaign conditions and schedule at https://oaei.ontologymatching.org/2022/.

Long Technical Papers:

• The impact of imbalanced class distribution on knowledge graphs matching
  Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner
• Exploring Wasserstein distance across concept embeddings for ontology matching
  Yuan An, Alex Kalinowski, Jane Greenberg
• LamAPI: a comprehensive tool for string-based entity retrieval with type-base filters
  Roberto Avogadro, Marco Cremaschi, Fabio D'Adda, Flavio De Paoli, Matteo Palmonari
• BiodivTab: semantic table annotation benchmark construction, analysis, and new additions
  Nora Abdelmageed, Sirko Schindler, Birgitta König-Ries
• An eye on representation learning in ontology matching
  Guilherme Sousa, Rinaldo Lima, Cassia Trojahn
• A simple standard for ontological mappings 2022: updates of data model and outlook
  Nicolas Matentzoglu, Joe Flack, John Graybeal, Nomi L. Harris, Harshad B. Hegde, Charles T. Hoyt, Hyeongsik Kim, Sabrina Toro, Nicole Vasilevsky, Christopher J. Mungall

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, Univeristy of London, UK & SIRIUS, Univeristy 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
• Majid Mohammadi, Eindhoven University of Technology, Netherlands
• Hoa Ngo, CSIRO, Australia
• George Papadakis, University of Athens, Greece
• Henry Rosales-Méndez, University of Chile, Chile
• Booma Sowkarthiga, Microsoft, USA
• Kavitha Srinivas, IBM, USA
• 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: