=Paper=
{{Paper
|id=Vol-3063/oaei21_paper10
|storemode=property
|title=LogMap family participation in the OAEI 2021
|pdfUrl=https://ceur-ws.org/Vol-3063/oaei21_paper10.pdf
|volume=Vol-3063
|authors=Ernesto Jiménez-Ruiz
|dblpUrl=https://dblp.org/rec/conf/semweb/Jimenez-Ruiz21
}}
==LogMap family participation in the OAEI 2021==
https://ceur-ws.org/Vol-3063/oaei21_paper10.pdf
LogMap Family Participation in the OAEI 2021 ?
Ernesto Jiménez-Ruiz1,2
1
Department of Computer Science, City, University of London, UK
2
Department of Informatics, University of Oslo, Oslo, Norway
Abstract. We present the participation of LogMap and its variants in the OAEI
2021 campaign. The LogMap project started in January 2011 with the objective
of developing a scalable and logic-based ontology matching system. This is the
eleventh participation in the OAEI and the experience has so far been very pos-
itive. LogMap is one of the few systems that participates in (almost) all OAEI
tracks.
1 Presentation of the system
LogMap [9, 11] is a highly scalable ontology matching system that implements the
consistency and locality principles [10]. LogMap is one of the few ontology matching
system that (i) can efficiently match semantically rich ontologies containing tens (and
even hundreds) of thousands of classes, (ii) incorporates sophisticated reasoning and
repair techniques to minimise the number of logical inconsistencies, and (iii) provides
support for user intervention during the matching process. LogMap ISWC 2011 paper
[9] has recently been awarded the SWSA Ten-Year Award.3
1.1 LogMap variants in the 2021 campaign
As in previous campaigns, in the OAEI 2021 we have participated with two additional
variants:
LogMapLt is a “lightweight” variant of LogMap, which essentially only applies (effi-
cient) string matching techniques.
LogMapBio includes an extension to use BioPortal [6, 7] as a (dynamic) provider of
mediating ontologies instead of relying on a few preselected ontologies [3].
In previous years we also participated with LogMapC4 .
?
Copyright c 2021 for this paper by its authors. Use permitted under Creative Commons Li-
cense Attribution 4.0 International (CC BY 4.0).
3
http://swsa.semanticweb.org/content/swsa-ten-year-award
4
LogMapC (https://github.com/asolimando/logmap-conservativity/) is
a variant of LogMap which, in addition to the consistency and locality principles, also imple-
ments the conservativity principle (see details in [13]).
1.2 Link to the system and parameters file
LogMap is open-source and released under the Apache-2.0 License.5 LogMap com-
ponents and source code are available from the LogMap’s GitHub page: https:
//github.com/ernestojimenezruiz/logmap-matcher/.
LogMap distributions can be easily customized through a configuration file contain-
ing the matching parameters.
LogMap, including support for interactive ontology matching, can also be used
directly through an AJAX-based Web interface: http://krrwebtools.cs.ox.
ac.uk/. This interface has been very well received by the community since it was
deployed in 2012. More than 5,500 requests coming from a broad range of users have
been processed so far.
1.3 LogMap as a mapping repair system
Only a very few systems participating in the OAEI competition implement repair tech-
niques. As a result, existing matching systems (even those that typically achieve very
high precision scores) compute mappings that lead in many cases to a large number of
unsatisfiable classes.
We believe that these systems could significantly improve their output if they were
to implement repair techniques similar to those available in LogMap. Therefore, with
the goal of providing a useful service to the community, we have made LogMap’s ontol-
ogy repair module (LogMap-Repair) available as a self-contained software component
that can be seamlessly integrated in most existing ontology matching systems [12, 5].
1.4 LogMap as a matching task division system
LogMap also includes a novel module to divide the ontology alignment task into (inde-
pendent) manageable subtasks [8]. This component relies on LogMap’s lexical index,
a neural embedding model [14] and locality-based modules [4]. This module can be
integrated within existing ontology alignment systems as a external module to support
them complete large-scale matching tasks.
2 General comments and conclusions
Please refer to http://oaei.ontologymatching.org/2021/results/ for
the results of the LogMap family in the OAEI 2021 campaign.
2.1 Comments on the results
As in previous campaigns, LogMap has been one of the top systems and one of the few
systems that participates in (almost) all tracks. Furthermore, it has also been one of the
few systems implementing repair techniques and providing (almost) coherent mappings
in all tracks.
5
http://www.apache.org/licenses/
LogMap’s main weakness is that the computation of candidate mappings is based
on the similarities between the vocabularies of the input ontologies; hence, in the cases
where the ontologies are lexically disparate or do not provide enough lexical informa-
tion LogMap is at a disadvantage.
2.2 Future work
LogMap is being extended in combination with machine learning techniques and ontol-
ogy embeddings based on language models (e.g., OWL2Vec* [1]) Preliminary results
of LogMap-ML have been recently published [2]. We aim at targeting the OAEI 2022
with a fully-fledged system.
Acknowledgements. I would also like to thank Bernardo Cuenca-Grau, Ian Horrocks,
Alessandro Solimando, Jiaoyan Chen, Valerie Cross, Anton Morant, Yujiao Zhou, Weiguo
Xia, Xi Chen, Yuan Gong and Shuo Zhang, who have contributed to the LogMap project
in the past.
References
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embedding of OWL ontologies. Mach. Learn. 110(7), 1813–1845 (2021)
2. Chen, J., Jiménez-Ruiz, E., Horrocks, I., Antonyrajah, D., Hadian, A., Lee, J.: Augmenting
ontology alignment by semantic embedding and distant supervision. In: 18th Extended Sem.
Web Conf. (ESWC). pp. 392–408 (2021)
3. Chen, X., Xia, W., Jiménez-Ruiz, E., Cross, V.: Extending an ontology alignment system
with bioportal: a preliminary analysis. In: Poster at Int’l Sem. Web Conf. (ISWC) (2014)
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