TY - JOUR
T1 - OntoConnect
T2 - 15th International Workshop on Ontology Matching, OM 2020
AU - Chakraborty, Jaydeep
AU - Yaman, Beyza
AU - Virgili, Luca
AU - Konar, Krishanu
AU - Bansal, Srividya K.
N1 - Funding Information:
Beyza Yaman has been supported by the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology [grant number 13/RC/2106] and Ordnance Survey Ireland.
Funding Information:
The authors gratefully acknowledge the Google Summer Code program and DBpedia organization for guidance and support. We also thank the Google Cloud Platform (GCP) research credits program for providing an environment to run the experiments using their Cloud Computing services. Beyza Yaman has been supported by the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology [grant number 13/RC/2106] and Ordnance Survey Ireland.
Publisher Copyright:
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2020
Y1 - 2020
N2 - The results of OntoConnect, an Ontology alignment system, in the Ontology Alignment Evaluation Initiative (OAEI) 2020 campaign is reported in this paper. OntoConnect is a domain-independent schema alignment system that combines syntactic similarity and structural similarity between classes/concepts to align the classes/concepts from the source and target ontologies. This paper describes the participation of OntoConnect at OAEI 2020 and discusses its methodology and results on the Anatomy dataset.
AB - The results of OntoConnect, an Ontology alignment system, in the Ontology Alignment Evaluation Initiative (OAEI) 2020 campaign is reported in this paper. OntoConnect is a domain-independent schema alignment system that combines syntactic similarity and structural similarity between classes/concepts to align the classes/concepts from the source and target ontologies. This paper describes the participation of OntoConnect at OAEI 2020 and discusses its methodology and results on the Anatomy dataset.
KW - Ontology Matching
KW - Ontology alignment
KW - Recursive Neural Network
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85099395191&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099395191&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85099395191
SN - 1613-0073
VL - 2788
SP - 204
EP - 210
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 2 November 2020
ER -