=Paper=
{{Paper
|id=Vol-2954/invited-2
|storemode=property
|title=Ontology Alignment and the two DLs (Abstract of Invited Talk)
|pdfUrl=https://ceur-ws.org/Vol-2954/invited-2.pdf
|volume=Vol-2954
|authors=Ernesto Jimenez-Ruiz
|dblpUrl=https://dblp.org/rec/conf/dlog/Jimenez-Ruiz21
}}
==Ontology Alignment and the two DLs (Abstract of Invited Talk)==
<pdf width="1500px">https://ceur-ws.org/Vol-2954/invited-2.pdf</pdf>
<pre>
               Ontology Alignment and the Two DLs
                    (Abstract of Invited Talk)?

                                   Ernesto Jiménez-Ruiz
                       1
                        City, University of London, United Kingdom
                      ernesto.jimenez-ruiz@city.ac.uk
              2
                SIRIUS, Department of Informatics, University of Oslo, Norway


The Ontology Matching community has been very active since the first steps of the
Semantic Web. The Ontology Alignment Evaluation Initiative (OAEI) has been running
annually since 2004.1 The objective of the OAEI is to perform a systematic evaluation
of ontology matching systems to conduct a comparison on the same basis and to enable
the reproducibility of the results. The OAEI 2020 included 12 tracks of different nature
and in a diverse set of domains, each of them including one or more matching tasks [1].
     Despite the amazing evaluation and system development efforts around the Ontol-
ogy Matching community, there are still several challenges that need to be tackled from
both the evaluation and system sides: (i) better connection with real-world needs and
user satisfaction, (ii) discovery of mappings beyond atomic subsumption and equiv-
alence, (iii) combination with machine learning methods, and (iv) awareness of the
logical compatibility of the ontologies.
     In the presentation I will give an overview of the OAEI and the above challenges
with a special focus on challenges (iii) and (iv), i.e., the two DLs (Deep Learning and
Description Logics). While Deep Learning techniques are introducing elegant solutions
with promising results [2], the Ontology Matching community should not forget about
the need of computing alignment sets that preserve the logical consistency (possibly
with only intended entailments) of the integrated ontology (assuming that the alignment
is interpreted as a set of Description Logic axioms) [3,4].

References
1. Abd Nikooie Pour, M., Algergawy, A., Amini, R., Faria, D., et al.: Results of the Ontology
   Alignment Evaluation Initiative 2020. In: Proceedings of the 15th International Workshop on
   Ontology Matching. (2020) 92–138
2. Chen, J., Jiménez-Ruiz, E., Horrocks, I., Antonyrajah, D., Hadian, A., Lee, J.: Augmenting
   Ontology Alignment by Semantic Embedding and Distant Supervision. In: The Semantic Web
   - 18th International Conference, ESWC 2021. (2021) 392–408
3. Jiménez-Ruiz, E., Meilicke, C., Cuenca Grau, B., Horrocks, I.: Evaluating Mapping Repair
   Systems with Large Biomedical Ontologies. In: Proceedings of the 26th International Work-
   shop on Description Logics. (2013) 246–257
4. Solimando, A., Jiménez-Ruiz, E., Guerrini, G.: Minimizing conservativity violations in on-
   tology alignments: algorithms and evaluation. Knowl. Inf. Syst. 51(3) (2017) 775–819
?
   Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons Li-
   cense Attribution 4.0 International (CC BY 4.0).
 1
   OAEI campaign: http://oaei.ontologymatching.org/

</pre>