Ontology Matching Organization 0 1 Pavel Shvaiko 0 1 Trentino Digitale SpA 0 1 Italy 0 1 Alsayed Algergawy 0 1 Jena University 0 1 Germany Manuel Atencia 0 1 University Grenoble Alpes 0 1 INRIA 0 1 France Zohra Bellahsene 0 1 LIRMM 0 1 France Jiaoyan Chen 0 1 University of Oxford 0 1 UK Valerie Cross 0 1 Miami University 0 1 USA Je´roˆme David 0 1 University Grenoble Alpes 0 1 INRIA 0 1 France Gayo Diallo 0 1 University of Bordeaux 0 1 France Daniel Faria 0 1 Instituto Gulbenkian de Cie´ncia 0 1 Portugal Alfio Ferrara 0 1 University of Milan 0 1 Italy Marko Gulic´ 0 1 University of Rijeka 0 1 Croatia Wei Hu 0 1 Nanjing University 0 1 China Ryutaro Ichise 0 1 National Institute of Informatics 0 1 Japan Antoine Isaac 0 1 Vrije Universiteit Amsterdam 0 1 Europeana 0 1 Netherlands Naouel Karam 0 1 Fraunhofer 0 1 Germany Prodromos Kolyvakis 0 1 Switzerland Patrick Lambrix 0 1 Linko¨pings Universitet 0 1 Sweden Oliver Lehmberg 0 1 University of Mannheim 0 1 Germany Majeed Mohammadi 0 1 TU Delft 0 1 Netherlands Peter Mork 0 1 MITRE 0 1 USA Andriy Nikolov 0 1 Metaphacts GmbH 0 1 Germany George Papadakis 0 1 University of Athens 0 1 Greece Catia Pesquita 0 1 University of Lisbon 0 1 Portugal 0 1 Ca ́ssia Trojahn , IRIT , France Oktie Hassanzadeh, IBM Research , USA 2020

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 translation, query answering or navigation over knowledge graphs. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate.

 Introduction 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 in

stance matching (link discovery) approaches through the OAEI (Ontology Alignment Evaluation Initiative) 2020 campaign2.

 To examine similarities and differences from other, old, new and emerging, tech

niques and usages, such as process matching, web table matching or knowledge embeddings.

The program committee selected 6 long and 4 short submissions for oral presentation and 6 submissions for poster presentation. 19 matching systems participated in this year’s OAEI campaign. Further information about the Ontology Matching workshop can be found at: http://om2020.ontologymatching.org/. Acknowledgments. We thank all members of the program committee, authors and local organizers for their efforts. We appreciate support from the Trentino as a Lab3 initiative of the European Network of the Living Labs4 at Trentino Digitale5, the EU SEALS (Semantic Evaluation at Large Scale) project6, the EU HOBBIT (Holistic Benchmarking of Big Linked Data) project7, the Pistoia Alliance Ontologies Mapping project8 and IBM Research9.

Pavel Shvaiko

Je´roˆme Euzenat Ernesto Jime´nez-Ruiz Oktie Hassanzadeh Ca´ssia Trojahn December 2020 3www.facebook.com/trentinoasalab 4www.openlivinglabs.eu 5www.trentinodigitale.it 6www.seals-project.eu 7https://project-hobbit.eu/challenges/om2020/ 8www.pistoiaalliance.org/projects/current-projects/ontologies-mapping 9research.ibm.com

ii

 Organizing Committee Je´roˆme Euzenat,

INRIA & University Grenoble Alpes, France

 Program Committee Henry Rosales-Me´ndez, University of Chile, Chile Kavitha Srinivas, IBM, USA

Giorgos Stoilos, Huawei Technologies, Greece Pedro Szekely, University of Southern California, USA

 Ludger van Elst, DFKI, Germany

Xingsi Xue, Fujian University of Technology, China Ondrˇej Zamazal, Prague University of Economics, Czech Republic Songmao Zhang, Chinese Academy of Sciences, China

 Using domain lexicon and grammar for ontology matching

Francisco Jose´ Quesada Real, Ga´bor Bella, Fiona McNeill, Alan Bundy . . . . . . . . . . . 1

 Semantic schema mapping for interoperable data-exchange

Harshvardhan J. Pandit, Damien Graux, Fabrizio Orlandi, Ademar Crotti Junior, Declan O’Sullivan, Dave Lewis . . . . . . . . . . . . . . . . . . . . . . . . . . 13

 A gold standard dataset for large knowledge graphs matching

Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 Applying edge-counting semantic similarities to link discovery: scalability and accuracy

Kleanthi Georgala, Mohamed Ahmed Sherif, Michael Ro¨der, Axel-Cyrille Ngonga Ngomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

 LIGON - link discovery with noisy oracles

Mohamed Ahmed Sherif, Kevin Dreßler, Axel-Cyrille Ngonga Ngomo . . . . . . . . . . . . 48

 Supervised ontology and instance matching with MELT

Sven Hertling, Jan Portisch, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Short Technical Papers

 SUBINTERNM: optimizing the matching of networks of ontologies

Fabio Santos, Kate Revoredo, Fernanda Baia˜o . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

 LIGER - link discovery with partial recall

Kleanthi Georgala, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo . . . . . . . . 87 OAEI Papers

 Results of the Ontology Alignment Evaluation Initiative 2020

Mina Abd Nikooie Pour, Alsayed Algergawy, Reihaneh Amini, Daniel Faria, Irini Fundulaki, Ian Harrow, Sven Hertling, Ernesto Jime´nez-Ruiz, Clement Jonquet, Naouel Karam, Abderrahmane Khiat, Amir Laadhar, Patrick Lambrix, Huanyu Li, Ying Li, Pascal Hitzler, Heiko Paulheim, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Andrea Splendiani, Elodie Thie´blin, Ca´ssia Trojahn, Jana Vatasˇcˇinova´, Beyza Yaman, Ondrˇej Zamazal, Lu Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

 ALIN results for OAEI 2020

Jomar da Silva, Carla Delgado, Kate Revoredo, Fernanda Baia˜o . . . . . . . . . . . . . . . 139

 ALOD2Vec matcher results for OAEI 2020

Jan Portisch, Michael Hladik, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

 OAEI 2020 results for AML and AMLC

Beatriz Lima, Daniel Faria, Francisco M. Couto, Isabel F. Cruz, Catia Pesquita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

 AROA results for OAEI 2020 Lu Zhou, Pascal Hitzler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 ATBox results for OAEI 2020 Sven Hertling, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Results of CANARD in OAEI 2020

Elodie Thie´blin, Ollivier Haemmerle´, Ca´ssia Trojahn . . . . . . . . . . . . . . . . . . . . . . . . . 176

 DESKMatcher

Michael Monych, Jan Portisch, Michael Hladik, Heiko Paulheim . . . . . . . . . . . . . . . 181

 FTRLIM results for OAEI 2020

Xiaowen Wang, Yizhi Jiang, Hongfei Fan, Hongming Zhu, Qin Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

 Lily results for OAEI 2020

Yunyan Hu, Shaochen Bai, Shiyi Zou, Peng Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

 LogMap family participation in the OAEI 2020 Ernesto Jime´nez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 OntoConnect: results for OAEI 2020

Jaydeep Chakraborty, Beyza Yaman, Luca Virgili, Krishanu Konar, Srividya Bansal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

 RE-miner for data linking results for OAEI 2020

Armita Khajeh Nassiri, Nathalie Pernelle, Fatiha Sa¨ıs, Gianluca Quercini . . . . . . . 211

 VeeAlign: a supervised deep learning approach to ontology alignment Vivek Iyer, Arvind Agarwal, Harshit Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Wiktionary matcher results for OAEI 2020 Jan Portisch, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 vii Ontology alignment in ecotoxicological effect prediction

Erik B. Myklebust, Ernesto Jime´nez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

 Towards semantic alignment of heterogeneous structures and its application to digital humanities Renata Vieira, Ca´ssia Trojahn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Ontology matching for the laboratory analytics domain

Ian Harrow, Thomas Liener, Ernesto Jime´nez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

 Towards matching of domain ontologies to cross-domain ontology: evaluation perspective

Martin Sˇatra, Ondrˇej Zamazal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

 Towards a vocabulary for mapping quality assessment

Alex Randles, Ademar Crotti Junior, Declan O’Sullivan . . . . . . . . . . . . . . . . . . . . . . . . 241