=Paper=
{{Paper
|id=Vol-2788/om2020_preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2788/om2020_preface.pdf
|volume=Vol-2788
}}
==None==
https://ceur-ws.org/Vol-2788/om2020_preface.pdf
Ontology Matching
OM-2020
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 align-
ment, that is, a set of correspondences between the semantically related entities of
those ontologies. These correspondences can be used for various tasks, such as ontol-
ogy 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.
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 on-
tology 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 Align-
ment 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 presenta-
tion 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/.
1 http://www.ontologymatching.org/
2 http://oaei.ontologymatching.org/2020
i
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
Jérôme Euzenat
Ernesto Jiménez-Ruiz
Oktie Hassanzadeh
Cássia Trojahn
December 2020
3 www.facebook.com/trentinoasalab
4 www.openlivinglabs.eu
5 www.trentinodigitale.it
6 www.seals-project.eu
7 https://project-hobbit.eu/challenges/om2020/
8 www.pistoiaalliance.org/projects/current-projects/ontologies-mapping
9 research.ibm.com
ii
Organization
Organizing Committee
Pavel Shvaiko,
Trentino Digitale SpA, Italy
Jérôme Euzenat,
INRIA & University 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, University Grenoble Alpes & INRIA, France
Zohra Bellahsene, LIRMM, France
Jiaoyan Chen, University of Oxford, UK
Valerie Cross, Miami University, USA
Jérôme David, University Grenoble Alpes & INRIA, France
Gayo Diallo, University of Bordeaux, France
Daniel Faria, Instituto Gulbenkian de Ciéncia, 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
Majeed Mohammadi, TU Delft, Netherlands
Peter Mork, MITRE, USA
Andriy Nikolov, Metaphacts GmbH, Germany
George Papadakis, University of Athens, Greece
Catia Pesquita, University of Lisbon, Portugal
iii
Henry Rosales-Méndez, 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
Ondřej Zamazal, Prague University of Economics, Czech Republic
Songmao Zhang, Chinese Academy of Sciences, China
iv
Table of Contents
Long Technical Papers
Using domain lexicon and grammar for ontology matching
Francisco José Quesada Real, Gábor 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 Röder,
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
Learning reference alignments for ontology matching
within and across domains
Beatriz Lima, Ruben Branco, João Castanheira, Gustavo Fonseca,
Catia Pesquita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
SUBINTERNM: optimizing the matching of networks of ontologies
Fabio Santos, Kate Revoredo, Fernanda Baião . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
A survey of OpenRefine reconciliation services
Antonin Delpeuch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
LIGER - link discovery with partial recall
Kleanthi Georgala, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo . . . . . . . . 87
v
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 Jiménez-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 Thiéblin, Cássia Trojahn, Jana Vataščinová, Beyza Yaman,
Ondřej Zamazal, Lu Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
ALIN results for OAEI 2020
Jomar da Silva, Carla Delgado, Kate Revoredo, Fernanda Baião . . . . . . . . . . . . . . . 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 Thiéblin, Ollivier Haemmerlé, Cássia 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 Jiménez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
OntoConnect: results for OAEI 2020
Jaydeep Chakraborty, Beyza Yaman, Luca Virgili, Krishanu Konar,
Srividya Bansal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
vi
RE-miner for data linking results for OAEI 2020
Armita Khajeh Nassiri, Nathalie Pernelle, Fatiha Saı̈s, 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
Posters
Ontology alignment in ecotoxicological effect prediction
Erik B. Myklebust, Ernesto Jiménez-Ruiz, Jiaoyan Chen, Raoul Wolf,
Knut Erik Tollefsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Towards semantic alignment of heterogeneous structures
and its application to digital humanities
Renata Vieira, Cássia Trojahn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Ontology matching for the laboratory analytics domain
Ian Harrow, Thomas Liener, Ernesto Jiménez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Towards matching of domain ontologies to cross-domain ontology:
evaluation perspective
Martin Šatra, Ondřej Zamazal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
Towards a vocabulary for mapping quality assessment
Alex Randles, Ademar Crotti Junior, Declan O’Sullivan . . . . . . . . . . . . . . . . . . . . . . . . 241
TableCNN: deep learning framework for learning tabular data
Pranav Sankhe, Elham Khabiri, Bhavna Agrawal, Yingjie Li . . . . . . . . . . . . . . . . . . . 243
viii
ix