Special issue on Ontology Matching and Machine Learning
https://www.semantic-web-journal.net/blog/special-issue-ontology-matching-a…
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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.
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Themes and Topics
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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
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Deadline
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Submission deadline: 20th February 2024. Papers submitted before the deadline will be
reviewed upon receipt.
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Author Guidelines
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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.
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Guest Editors
The guest editors can be reached at om-ml(a)googlegroups.com .
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Cássia Trojahn, IRIT, France
Sven Hertling, University of Mannheim, Germany
Huanyu Li, Linköping University, Sweden
Oktie Hassanzadeh, IBM Research, USA
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Guest Editorial Board
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Ernesto Jiménez-Ruiz, City, University of London, UK & SIRIUS, University of Oslo,
Norway
Pavel Shvaiko, Trentino Digitale, Italy
Jérôme Euzenat, INRIA & Univ. Grenoble Alpes, France
TO BE COMPLETED