Ontology Matching Organization 0 1 Pavel Shvaiko 0 1 Trentino Digitale SpA 0 1 Italy 0 1 Ca ́ssia Trojahn , IRIT , France Oktie Hassanzadeh, IBM Research , USA 2019

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) 2019 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 3 long and 2 short submissions for oral presentation and 7 submissions for poster presentation. 20 matching systems participated in this year’s OAEI campaign. Further information about the Ontology Matching workshop can be found at: http://om2019.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 2019 3http://www.taslab.eu 4http://www.openlivinglabs.eu 5http://www.trentinodigitale.it 6http://www.seals-project.eu 7https://project-hobbit.eu/challenges/om2019/ 8http://www.pistoiaalliance.org/projects/ontologies-mapping/ 9research.ibm.com

ii

 Organizing Committee Je´roˆme Euzenat,

INRIA & University Grenoble Alpes, France

 Program Committee Peter Mork, MITRE, USA Andriy Nikolov, Metaphacts GmbH, Germany Axel Ngonga, University of Paderborn, Germany George Papadakis, University of Athens, Greece Catia Pesquita, University of Lisbon, Portugal Henry Rosales-Me´ndez, University of Chile, Chile Juan Sequeda, data.world, USA Kavitha Srinivas, IBM, USA Giorgos Stoilos, National Technical University of Athens, Greece Pedro Szekely, University of Southern California, USA Valentina Tamma, University of Liverpool, UK 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 iv Multi-view embedding for biomedical ontology matching

Weizhuo Li, Xuxiang Duan, Meng Wang, XiaoPing Zhang, Guilin Qi . . . . . . . . . . . . . 13

 Identifying mappings among knowledge graphs by formal concept analysis Guowei Chen, Songmao Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Short Technical Papers Hypernym relation extraction for establishing subsumptions: preliminary results on matching foundational ontologies

Mouna Kamel, Daniela Schmidt, Ca´ssia Trojahn, Renata Vieira . . . . . . . . . . . . . . . . . 36

 Generating corrupted data sources for the evaluation of matching systems

Fiona McNeill, Diana Bental, Alasdair Gray, Sabina Jedrzejczyk, Ahmad Alsadeeqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

OAEI Papers Results of the Ontology Alignment Evaluation Initiative 2019

Alsayed Algergawy, Daniel Faria, Alfio Ferrara, Irini Fundulaki, Ian Harrow, Sven Hertling, Ernesto Jime´nez-Ruiz, Naouel Karam, Abderrahmane Khiat, Patrick Lambrix, Huanyu Li, Stefano Montanelli, Heiko Paulheim, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Andrea Splendiani, Elodie Thie´blin, Ca´ssia Trojahn, Jana Vatasˇcˇinova´, Ondrˇej Zamazal, Lu Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

 AnyGraphMatcher submission to the OAEI knowledge graph challenge 2019 Alexander Lu¨tke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 ALIN results for OAEI 2019

Jomar da Silva, Carla Delgado, Kate Revoredo, Fernanda Baia˜o . . . . . . . . . . . . . . . . 94

 AML and AMLC results for OAEI 2019

Daniel Faria, Catia Pesquita, Teemu Tervo, Francisco M. Couto, Isabel F. Cruz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

 AROA results for 2019 OAEI

Lu Zhou, Michelle Cheatham, Pascal Hitzler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

 CANARD complex matching system: results of the 2019 OAEI evaluation campaign

Elodie Thie´blin, Ollivier Haemmerle´, Ca´ssia Trojahn . . . . . . . . . . . . . . . . . . . . . . . . . 114

 DOME results for OAEI 2019 Sven Hertling, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 EVOCROS: results for OAEI 2019

Juliana Medeiros Destro, Javier A. Vargas, Julio Cesar dos Reis, Ricardo da S. Torres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

 FCAMap-KG results for OAEI 2019 Fei Chang, Guowei Chen, Songmao Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 FTRLIM results for OAEI 2019

Xiaowen Wang, Yizhi Jiang, Yi Luo, Hongfei Fan, Hua Jiang, Hongming Zhu, Qin Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

 Lily results for OAEI 2019

Jiangheng Wu, Zhe Pan, Ce Zhang, Peng Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

 LogMap family participation in the OAEI 2019 Ernesto Jime´nez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 vi ONTMAT1: results for OAEI 2019 Saida Gherbi, Mohamed Tarek Khadir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 POMap++ results for OAEI 2019: fully automated machine learning approach for ontology matching

Amir Laadhar, Faiza Ghozzi, Imen Megdiche, Franck Ravat, Olivier Teste, Faiez Gargouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

 SANOM results for OAEI 2019

Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, Yao-Hua Tan . . . . . . . . . 175

 Wiktionary matcher

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

 MultiKE: a multi-view knowledge graph embedding framework for entity alignment

Wei Hu, Qingheng Zhang, Zequn Sun, Jiacheng Huang . . . . . . . . . . . . . . . . . . . . . . . . 189

 MTab: matching tabular data to knowledge graph with probability models

Phuc Nguyen, Natthawut Kertkeidkachorn, Ryutaro Ichise, Hideaki Takeda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

 Generating referring expressions from knowledge graphs

Armita Khajeh Nassiri, Nathalie Pernelle, Fatiha Sa¨ıs . . . . . . . . . . . . . . . . . . . . . . . . . 193

 Semantic table interpretation using MantisTable

Marco Cremaschi, Anisa Rula, Alessandra Siano, Flavio De Paoli . . . . . . . . . . . . . . 195

 Towards explainable entity matching via comparison queries

Alina Petrova, Egor V. Kostylev, Bernardo Cuenca Grau, Ian Horrocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

 Discovering expressive rules for complex ontology matching and data interlinking

Manuel Atencia, Je´roˆme David, Je´roˆme Euzenat, Liliana Ibanescu, Nathalie Pernelle, Fatiha Sa¨ıs, Elodie Thie´blin, Ca´ssia Trojahn . . . . . . . . . . . . . . . . 199

 Decentralized reasoning on a network of aligned ontologies with link keys

Je´re´my Lhez, Chan Le Duc, Thinh Dong, Myriam Lamolle . . . . . . . . . . . . . . . . . . . . . 201