Special issue on Ontology Matching and Machine Learning

Call for papers: Special Issue on

Ontology Matching and Machine Learning

This special issue aims to discuss the latest research proposals and on the use of machine learning for ontology matching, data interlinking, and data integration in general.

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.

While early approaches have addressed the use of machine learning, new deep learning and large language models have gained attention in the field, proving new ways of capturing the relationships between the entities of different ontologies.

The special issue aims at providing a comprehensive view of the latest research advancements and inspire further research in this evolving area. We welcome original research papers that propose novel techniques, models, and frameworks for ontology matching, data interlink and data integration.

Themes and Topics

We are interested in (including but not limited to) the following themes and topics that study the application of deep learning and large langage models in general:

- Matching and deep learning
- Matching and large language models
- Learning in instance matching, data interlinking
- Large-scale and efficient matching techniques
- Matching and neuro-symbolic techniques
- Matcher selection, combination and tuning
- User involvement
- Explanations in matching
- Social and collaborative matching
- Uncertainty in matching
- Expressive alignments
- Reasoning with alignments
- Alignment coherence and debugging
- Matching for emerging applications (e.g., web tables, knowledge graphs)
- Benchmarks for machine learning oriented matching


  • Submission deadline: 20th February 2024. Papers submitted before the deadline will be reviewed upon receipt.

Author Guidelines

We invite full papers, dataset descriptions, application reports and reports on tools and systems. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this special issue. Authors can extend previously published conference or workshop papers; guidelines for this can be found in FAQ 9

Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors.

We welcome any submission type as described http://www.semantic-web-journal.net/authors#types.

While there is no upper limit, paper length must be justified by content. Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Ontology Matching and Machine Learning" special issue.

All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.

Also note that the Semantic Web journal is open access and all submissions rely on an open and transparent review process (see FAQ 1). Finally please note that submissions must comply with the journal’s Open Science Data requirements, which are detailed in the corresponding blog post.

Guest Editors

The guest editors can be reached at om-ml@googlegroups.com .

Cássia Trojahn, IRIT, France
Sven Hertling, University of Mannheim, Germany
Huanyu Li, Linköping University, Sweden
Oktie Hassanzadeh, IBM Research, USA

Guest Editorial Board

Ernesto Jiménez-Ruiz, City, Univeristy of London, UK & SIRIUS, Univeristy of Oslo, Norway
Pavel Shvaiko, Trentino Digitale, Italy
Jérôme Euzenat, INRIA & Univ. Grenoble Alpes, France
Vasilis Efthymiou, Harokopio University of Athens, Greece
George Papadakis, National and Kapodistrian University of Athens, Greece
Heiko Paulheim, University of Mannheim, Germany
Catia Pesquita, Universidade de Lisboa, Portugal
Pierre Monnin, Université Côte d’Azur, INRIA, France
Alsayed Algergawy, University of Passau, Germany
Yuan He, University of Oxford, United Kingdom
Jiaoyan Chen, University of Oxford, United Kingdom
Zhu Wang, University of Illinois, Chicago, USA
Valentina Tamma, University of Liverpool United Kingdom
Olivier Teste, Institut de Recherche en Informatique de Toulouse, France