=Paper=
{{Paper
|id=Vol-3324/om2022_preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-3324/om2022_preface.pdf
|volume=Vol-3324
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-3324/om2022_preface.pdf</pdf>
<pre>
                                      Ontology Matching
                                            OM-2022


                             Proceedings of the ISWC Workshop


Introduction
Ontology matching1 is a key interoperability enabler for the semantic web, as well as a
useful tactic 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.

      The workshop had three goals:

       • To bring together leaders from academia, industry and user institutions to assess how
         academic advances are addressing real-world requirements. The workshop strives to
         improve academic awareness of industrial and final user needs, and therefore, direct
         research towards those needs. Simultaneously, the workshop serves to inform industry
         and user representatives about existing research efforts that may meet their requirements.
         The workshop also investigated how the ontology matching technology is going to evolve.
       • To conduct an extensive and rigorous evaluation of ontology matching and instance
         matching (link discovery) approaches through the OAEI (Ontology Alignment Evaluation
         Initiative) 2022 campaign2 .
       • To examine similarities and differences from other, old, new and emerging, techniques
         and usages, such as process matching, web table matching or knowledge embeddings.

  The program committee selected 9 submissions for oral presentation and 4 submissions for
poster presentation. 18 matching systems participated in this year’s OAEI campaign. Fur-
ther information about the Ontology Matching workshop can be found at: http://om2022.
ontologymatching.org/.


1
    http://www.ontologymatching.org/
2
    http://oaei.ontologymatching.org/2022
Acknowledgments. We thank all members of the program committee, authors and local
organizers for their efforts. We appreciate support from Trentino Digitale3 , the EU SEALS
(Semantic Evaluation at Large Scale) project4 , the EU HOBBIT (Holistic Benchmarking of Big
Linked Data) project5 , the MELT (Matching EvaLuation Toolkit) project6 , the Pistoia Alliance
Ontologies Mapping project7 , IBM Research8 and SIRIUS Centre for Scalable Data Access9 .


Pavel Shvaiko
Jér^ome Euzenat
Ernesto Jiménez-Ruiz
Oktie Hassanzadeh
Cássia Trojahn

December 2022


3
  www.trentinodigitale.it
4
  www.seals-project.eu
5
  https://project-hobbit.eu/challenges/om2020/
6
  https://dwslab.github.io/melt/
7
  www.pistoiaalliance.org/projects/current-projects/ontologies-mapping
8
  research.ibm.com
9
  https://www.mn.uio.no/sirius/
                                   Organization


Organizing Committee
Pavel Shvaiko,
Trentino Digitale SpA, Italy

Jér^ome Euzenat,
INRIA & University 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
Jér^ome 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
Majeed 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
                                                    Table of Contents


Long Technical Papers

The impact of imbalanced class distribution on knowledge graphs matching
Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Exploring Wasserstein distance across concept embeddings
for ontology matching
Yuan An, Alex Kalinowski, Jane Greenberg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

BiodivTab: semantic table annotation benchmark construction,
analysis, and new additions
Nora Abdelmageed, Sirko Schindler, Birgitta König-Ries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

An eye on representation learning in ontology matching
Guilherme Sousa, Rinaldo Lima, Cássia Trojahn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61


Short Technical Papers

Too big to match: a strategy around matching tasks for large taxonomies
Alsayed Algergawy, Naouel Karam, Amir Laadhar, Franck Michel . . . . . . . . . . . . . . . . . . . . . . . . 67

Self-learning ontological concept representation for searching and matching tasks
Duy-Hoa Ngo, Bevan Koopman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Matching pharmacogenomic knowledge: particularities, results, and perspectives
Pierre Monnin, Adrien Coulet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
OAEI Papers

Results of the Ontology Alignment Evaluation Initiative 2022
Mina Abd Nikooie Pour, Alsayed Algergawy, Patrice Buche,
Leyla J. Castro, Jiaoyan Chen, Hang Dong, Omaima Fallatah,
Daniel Faria, Irini Fundulaki, Sven Hertling, Yuan He, Ian Horrocks,
Martin Huschka, Liliana Ibanescu, Ernesto Jiménez-Ruiz, Naouel Karam,
Amir Laadhar, Patrick Lambrix, Huanyu Li, Ying Li, Franck Michel, Engy Nasr,
Heiko Paulheim, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko,
Cássia Trojahn, Chantelle Verhey, Mingfang Wu, Beyza Yaman,
Ondřej Zamazal, Lu Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

ALIN results for OAEI 2022
Jomar da Silva, Kate Revoredo, Fernanda Baião, Cabral Lima . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

A-LIOn - alignment learning through inconsistency negatives
of the aligned ontologies
Sarah M. Alghamdi, Fernando Zhapa-Camacho, Robert Hoehndorf . . . . . . . . . . . . . . . . . . . . . . . 137

AMD results for OAEI 2022
Zhu Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

ATBox results for OAEI 2022
Sven Hertling, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

Cross-lingual ontology matching with CIDER-LM: results for OAEI 2022
Javier Vela, Jorge Gracia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

DLinker results for OAEI 2022
Bill Happi, Géraud Fokou Pelap, Danai Symeonidou, Pierre Larmande . . . . . . . . . . . . . . . . . . . . 166

GraphMatcher: a graph representation learning approach for ontology matching
Sefika Efeoglu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

KGMatcher+ results for OAEI 2022
Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

LogMap family participation in the OAEI 2022
Ernesto Jiménez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
LSMatch and LSMatch-multilingual results for OAEI 2022
Abhisek Sharma, Archana Patel, Sarika Jain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Matcha and Matcha-DL results for OAEI 2022
Daniel Faria, Marta Contreiras Silva, Pedro Cotovio,
Patrı́cia Eugénio, Catia Pesquita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

SEBMatcher results for OAEI 2022
Francis Gosselin, Amal Zouaq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

TOMATO: results of the 2022 OAEI evaluation campaign
Philippe Roussille, Olivier Teste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

WomboCombo results for OAEI 2022
Peter Kardos, Zsolt Szántó, Richárd Farkas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
Posters

What should be the minimum requirements for making FAIR ontology alignments?
Cássia Trojahn, Nicolas Matentzoglu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

Towards a unified metadata model for semantic and data mappings
Sarah Alzahrani, Declan O’Sullivan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Meta2KG: transforming metadata to knowledge graphs
Nora Abdelmageed, Birgitta König-Ries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226

Multifarm11 - extending the multifarm benchmark for Hindi language
Abhisek Sharma, Sarika Jain, Cássia Trojahn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

</pre>