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) 2021 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 5 submissions for oral presentation and 6 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://om2021.ontologymatching.org/. 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.
Je´roˆme Euzenat Ernesto Jime´nez-Ruiz Oktie Hassanzadeh Ca´ssia Trojahn December 2021 3www.trentinodigitale.it 4www.seals-project.eu 5https://project-hobbit.eu/challenges/om2020/ 6https://dwslab.github.io/melt/ 7www.pistoiaalliance.org/projects/current-projects/ontologies-mapping 8research.ibm.com 9https://www.mn.uio.no/sirius/
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Je´roˆme Euzenat, INRIA & University Grenoble Alpes, France
Kavitha Srinivas, IBM, USA 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
Biomedical ontology alignment with BERT Yuan He, Jiaoyan Chen, Denvar Antonyrajah, Ian Horrocks . . . . . . . . . . . . . . . . . . . . . . 1 Matching with transformers in MELT Sven Hertling, Jan Portisch, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 A hybrid approach for large knowledge graphs matching Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Challenges of evaluating complex alignments Beatriz Lima, Daniel Faria, Catia Pesquita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Results of the Ontology Alignment Evaluation Initiative 2021 Mina Abd Nikooie Pour, Alsayed Algergawy, Florence Amardeilh, Reihaneh Amini, Omaima Fallatah, Daniel Faria, Irini Fundulaki, Ian Harrow, Sven Hertling, Pascal Hitzler, Martin Huschka, Liliana Ibanescu, Ernesto Jime´nez-Ruiz, Naouel Karam, Amir Laadhar, Patrick Lambrix, Huanyu Li, Ying Li, Franck Michel, Engy Nasr, Heiko Paulheim, Catia Pesquita, Jan Portisch, Catherine Roussey, Tzanina Saveta, Pavel Shvaiko, Andrea Splendiani, Ca´ssia Trojahn, Jana Vatasˇcˇinova´, Beyza Yaman, Ondrˇej Zamazal, Lu Zhou . . . . . . . . . . . . . . . . . . . . . 62 ALIN results for OAEI 2021 Jomar da Silva, Kate Revoredo, Fernanda Baia˜o, Cabral Lima . . . . . . . . . . . . . . . . . 109 ALOD2vec matcher results for OAEI 2021 Jan Portisch, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 AgreementMakerDeep results for OAEI 2021 Zhu Wang, Isabel F. Cruz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 AML and AMLC results for OAEI 2021 Daniel Faria, Beatriz Lima, Marta Contreiras Silva, Francisco Couto, Catia Pesquita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 ATBox results for OAEI 2021 Sven Hertling, Heiko Paulheim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Fine-TOM matcher results for OAEI 2021 Leon Knorr, Jan Portisch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 GMap results for OAEI 2021 Weizhuo Li, Shiqi Zhou, Qiu Ji, Bingjie Lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 KGMatcher results for OAEI 2021 Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Lily results for OAEI 2021 Shiyi Zou, Jiajun Liu, Zherui Yang, Yunyan Hu, Peng Wang . . . . . . . . . . . . . . . . . . . . 167 LogMap family participation in the OAEI 2021 Ernesto Jime´nez-Ruiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 LSMatch results for OAEI 2021 Abhisek Sharma, Archana Patel, Sarika Jain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 OTMapOnto: optimal transport-based ontology matching Yuan An, Alex Kalinowski, Jane Greenberg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 TOM matcher results for OAEI 2021 Daniel Kossack, Niklas Borg, Leon Knorr, Jan Portisch . . . . . . . . . . . . . . . . . . . . . . . . 193 Combining FCA-Map with representation learning for aligning large biomedical ontologies Guoxuan Li, Songmao Zhang, Jiayi Wei, Wenqian Ye . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Integrating knowledge graphs for explainable artificial intelligence in biomedicine Marta Contreiras Silva, Daniel Faria, Catia Pesquita . . . . . . . . . . . . . . . . . . . . . . . . . 209 Concept for metadata and time series data integration based on a material science application ontology Paul Zierep, Dirk Helm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Bootstrapping supervised product taxonomy mapping with hierarchical path translations for the regulatory intelligence domain Alfredo Maldonado, Spencer Sharpe, Paul ter Horst . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 State-of-the-art instance matching methods for knowledge graphs Alex Boyko, Siamak Farshidi, Zhiming Zhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 ThValRec: threshold value recommendation approach for ontology matching Kumar Vidhani, Gurpriya Bhatia, Mangesh Gharote, Sachin Lodha . . . . . . . . . . . . . 217