The Fifteenth International Workshop on Ontology Matching

collocated with the 19th International Semantic Web Conference ISWC-2020
November 2nd, 2020, Virtual Conference (originally planned to be in Athens, Greece)

Download OM-2020 proceedings: CEUR-WS Vol-2788

Objectives Call for papers Submissions Accepted papers Program Organization OM-2019


Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful technique 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 has three goals:
  • To bring together leaders from academia, industry and user institutions to assess how academic advances are addressing real-world requirements. The workshop will strive to improve academic awareness of industrial and final user needs, and therefore, direct research towards those needs. Simultaneously, the workshop will serve to inform industry and user representatives about existing research efforts that may meet their requirements. The workshop will also investigate how the ontology matching technology is going to evolve, especially with respect to data interlinking, process mapping and web table matching tasks.

  • To conduct an extensive and rigorous evaluation of ontology matching and instance matching (link discovery) approaches through the OAEI (Ontology Alignment Evaluation Initiative) 2020 campaign. Besides real-world specific matching tasks, it features the second edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching track.

  • To examine similarities and differences from other, old, new and emerging, techniques and usages, such as process matching, web table matching or knowledge embeddings.

Call for papers


The workshop encourages participation from academia, industry and user institutions with the emphasis on theoretical and practical aspects of ontology matching. On the one side, we expect representatives from industry and user organizations to present business cases and their requirements for ontology matching. On the other side, we expect academic participants to present their approaches vis-a-vis those requirements. The workshop provides an informal setting for researchers and practitioners from different related initiatives to meet and benefit from each other's work and requirements.

This year, in sync with the main conference, we encourage submissions specifically devoted to: (i) datasets, benchmarks and replication studies, services, software, methodologies, protocols and measures (not necessarily related to OAEI), and (ii) application of the matching technology in real-life scenarios and assessment of its usefulness to the final users.

Topics of interest include but are not limited to:

  • Business and use cases for matching (e.g., big, open, closed data);
  • Requirements to matching from specific application scenarios (e.g., public sector, homeland security);
  • Application of matching techniques in real-world scenarios (e.g., in cloud, with mobile apps);
  • Formal foundations and frameworks for matching;
  • Novel matching methods, including link prediction, ontology-based data access;
  • Matching and knowledge graphs;
  • Matching and deep learning;
  • Matching and embeddings;
  • Matching and big data;
  • Matching and linked data;
  • Instance matching, data interlinking and relations between them;
  • Privacy-aware matching;
  • Process model matching;
  • Large-scale and efficient matching techniques;
  • Matcher selection, combination and tuning;
  • User involvement (including both technical and organizational aspects);
  • Explanations in matching;
  • Social and collaborative matching;
  • Uncertainty in matching;
  • Expressive alignments;
  • Reasoning with alignments;
  • Alignment coherence and debugging;
  • Alignment management;
  • Matching for traditional applications (e.g., data science);
  • Matching for emerging applications (e.g., web tables, knowledge graphs).

Contributions to the workshop can be made in terms of technical papers and posters/statements of interest addressing different issues of ontology matching as well as participating in the OAEI 2020 campaign. Long technical papers should be of max. 12 pages. Short technical papers should be of max. 5 pages. Posters/statements of interest should not exceed 2 pages. All contributions have to be prepared using the LNCS Style and should be submitted in PDF format (no later than August 10th, 2020) through the workshop submission site at:

Contributors to the OAEI 2020 campaign have to follow the campaign conditions and schedule at

Important dates:

  • August 17th 2020: CLOSED
    Deadline for the submission of papers
  • September 11th, 2020: Notifications have been sent out
    Deadline for the notification of acceptance/rejection
  • September 21st, 2020: CLOSED
    Workshop camera ready copy submission
  • October 30th, 2020: CLOSED
    ISWC'20 registration deadline
  • November 2nd, 2020:
    OM-2020: Virtual Conference run on Zoom and Remo

Contributions will be refereed by the Program Committee. Accepted papers will be published in the workshop proceedings as a volume of CEUR-WS as well as indexed on DBLP. By submitting a paper, the authors accept the CEUR-WS and DBLP publishing rules (CC-BY 4.0 license model).

Accepted Papers

Long Technical Papers:

Short Technical Papers:

OAEI Papers:

Abstracts (ex-posters):

CST, Shanghai EST, New York CET, Rome Links Schedule
20:45-21:00 7:45-8:00 13:45-14:00 Zoom Welcome and workshop overview Organizers
21:00-22:00 8:00-9:00 14:00-15:00 Zoom Keynote address by AnHai Doan
Magellan: Toward a System Building Agenda for Semantic Matching
Abstract: Semantic matching finds schema/data elements that refer to the same real-world concepts. Variations of this problem include string matching, entity matching, schema/ontology matching, matching tuples in a table into a knowledge base, and more. In the past five years, we have been building Magellan, a general platform that uses machine learning, big data processing, cloud technologies, and effective user interaction to solve semantic matching problems, focusing on entity matching (EM). Magellan has been deployed at 12 companies and domain science groups, used in high-profile projects (such as a recent effort to save the Amazon rainforest), recently commercialized by GreenBay Technologies, and pushed into EM platforms at Informatica, the world-leading data integration company. In this talk, I will discuss how Magellan is radically different from current EM work. Specifically, I discuss how we focus on "very boring" EM problems, identify the end-to-end process that a user must follow to solve them, then develop semi-automatic tools to support the various steps in the process. I will also discuss why we designed tools to be atomic, highly interoperable, and built into popular ecosystems of data science tools. Finally, I discuss lessons learned, both from Magellan and from my semantic matching work at Informatica. I will also touch on our ongoing effort to extend Magellan to other semantic matching problems, including ontology/schema matching.

Bio: AnHai Doan is Vilas Distinguished Achievement Professor and Gurindar S. Sohi Professor of Computer Science at the University of Wisconsin-Madison. His interests cover databases, AI, and Web, with a current focus on data integration, data science, and machine learning. AnHai received the ACM Doctoral Dissertation Award in 2003, a CAREER Award in 2004, and a Sloan Fellowship in 2007. He co-authored ''Principles of Data Integration'', a Morgan-Kaufmann textbook in 2012. AnHai was on the Advisory Board of Transformic, a Deep Web startup acquired by Google in 2005, and was Chief Scientist of Kosmix, a social media startup acquired by Walmart in 2011. From 2011 to 2014 he was Chief Scientist of WalmartLabs, a newly formed R&D lab that analyzes and integrates data for e-commerce. From 2019 until 2020 he was a co-founder of GreenBay Technologies, a data integration startup acquired by Informatica. AnHai serves on the SIGMOD Advisory Committee, SIGMOD Executive Committee, and co-chaired SIGMOD-2020.

22:00-00:00 9:00-11:00 15:00-17:00 Zoom Paper presentation session: Methods and Applications - I
22:00-22:20 9:00-9:20 15:00-15:20 Using domain lexicon and grammar for ontology matching
Francisco José Quesada Real, Gábor Bella, Fiona McNeill, Alan Bundy
22:20-22:40 9:20-9:40 15:20-15:40 Semantic schema mapping for interoperable data-exchange
Harshvardhan J. Pandit, Damien Graux, Fabrizio Orlandi, Ademar Crotti Junior, Declan O'Sullivan, Dave Lewis
22:40-23:00 9:40-10:00 15:40-16:00 A gold standard dataset for large knowledge graphs matching
Omaima Fallatah, Ziqi Zhang, Frank Hopfgartner
23:00-23:20 10:00-10:20 16:00-16:20 Applying edge-counting semantic similarities to link discovery: scalability and accuracy
Kleanthi Georgala, Mohamed Ahmed Sherif, Michael Röder, Axel-Cyrille Ngonga Ngomo
23:20-23:40 10:20-10:40 16:20-16:40 LIGON - link discovery with noisy oracles
Mohamed Ahmed Sherif, Kevin Dreßler, Axel-Cyrille Ngonga Ngomo
23:40-00:00 10:40-11:00 16:40-17:00 Supervised ontology and instance matching with MELT
Sven Hertling, Jan Portisch, Heiko Paulheim
00:00-00:15 11:00-11:15 17:00-17:15 Zoom Summary of the OAEI 2020 campaign and the SemTab challenge
00:15-00:30 11:15-11:30 17:15-17:30 Remo Break
00:30-01:30 11:30-12:30 17:30-18:30 Remo Parallel sessions (Remo tables): OAEI and Abstracts
AML is breaking rules: taking complex ontology Alignment beyond rule-based approaches for OAEI 2020
Beatriz Lima, Daniel Faria, Catia Pesquita
VeeAlign: a supervised deep learning approach to ontology alignment
Vivek Iyer, Arvind Agarwal, Harshit Kumar
Wiktionary matcher
Jan Portisch
bbw: matching CSV to Wikidata via meta-lookup
Renat Shigapov, Philipp Zumstein, Jan Kamlah, Lars Oberländer, Jörg Mechnich, Irene Schumm
JenTab: matching tabular data to knowledge graphs
Nora Abdelmageed
AMALGAM: a matching approach to fairfy tabular data with knowledge graph model
Gayo Diallo, Rabia Azzi
Towards a novel OAEI track for the materials' sciences and engineering domain
Engy Nasr
Ontology alignment in ecotoxicological effect prediction
Erik B. Myklebust, Ernesto Jiménez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen
Towards semantic alignment of heterogeneous structures and its application to digital humanities
Renata Vieira, Cássia Trojahn
Ontology matching for the laboratory analytics domain
Ian Harrow, Thomas Liener, Ernesto Jiménez-Ruiz
Towards matching of domain ontologies to cross-domain ontology: evaluation perspective
Martin Šatra, Ondřej Zamazal
Towards a vocabulary for mapping quality assessment
Alex Randles, Ademar Crotti Junior, Declan O'Sullivan
TableCNN: deep learning framework for learning tabular data
Pranav Sankhe, Elham Khabiri, Bhavna Agrawal, Yingjie Li
Revisiting ontologies of units of measure for harmonising quantity values - a use case
Francisco Martin-Recuerda, Dirk Walther, Siegfried Eisinger, Graham Moore, Petter Andersen, Per-Olav Opdahl, Lillian Hella
01:30-02:30 12:30-13:30 18:30-19:30 Zoom Paper presentation session: Methods and Applications - II
01:30-01:45 12:30-12:45 18:30-18:45 Learning reference alignments for ontology matching within and across domains
Beatriz Lima, Ruben Branco, João Castanheira, Gustavo Fonseca, Catia Pesquita
01:45-02:00 12:45-13:00 17:45-18:00 SUBINTERNM: optimizing the matching of networks of ontologies
Fabio Santos, Kate Revoredo, Fernanda Baião
02:00-02:15 13:00-13:15 19:00-19:15 A survey of OpenRefine reconciliation services
Antonin Delpeuch
02:15-02:30 13:15-13:30 19:15-19:30 LIGER - link discovery with partial recall
Kleanthi Georgala, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo
02:30-03:00 13:30-14:00 19:30-20:00 Zoom Discussion and wrap-up

Organizing Committee:

  • Pavel Shvaiko (Main contact)
    Trentino Digitale, Italy
    E-mail: pavel [dot] shvaiko [at] tndigit [dot] it
  • Jérôme Euzenat
    INRIA & Univ. 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, INRIA & Univ. Grenoble Alpes, 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
  • 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


We appreciate support from the Trentino as a Lab initiative of the European Network of the Living Labs at Trentino Digitale, the EU SEALS project, as well as the Pistoia Alliance Ontologies Mapping project and IBM Research.

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