Results 11 to 20 of about 42,984 (264)

Matching disease and phenotype ontologies in the ontology alignment evaluation initiative [PDF]

Journal of Biomedical Semantics, 2017
Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies.
Ian Harrow, Ernesto Jiménez-Ruiz, Andrea Splendiani, Martin Romacker, Peter Woollard, Scott Markel, Yasmin Alam-Faruque, Martin Koch, James Malone, Arild Waaler   +9 more
doaj   +12 more sources

Experiences from the anatomy track in the ontology alignment evaluation initiative [PDF]

Journal of Biomedical Semantics, 2017
Background One of the longest running tracks in the Ontology Alignment Evaluation Initiative is the Anatomy track which focuses on aligning two anatomy ontologies. The Anatomy track was started in 2005.
Zlatan Dragisic, Valentina Ivanova, Huanyu Li, Patrick Lambrix   +3 more
doaj   +6 more sources

Biomedical ontology alignment: an approach based on representation learning [PDF]

Journal of Biomedical Semantics, 2018
Background While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching.
Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith, Dimitris Kiritsis   +3 more
doaj   +3 more sources

Results of the Ontology Alignment Evaluation Initiative 2015 [PDF]

, 2015
cheatham2016aInternational audienceOntology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases.
Cheatham, Michelle, Dragisic, Zlatan, Euzenat, Jérôme, Faria, Daniel, Ferrara, Alfio, Flouris, Giorgos, Fundulaki, Irini, Granada, Roger, Ivanova, Valentina, Jiménez-Ruiz, Ernesto, Lambrix, Patrick, Montanelli, Stefano, Pesquita, Catia, Saveta, Tzanina, Shvaiko, Pavel, Solimando, Alessandro, Trojahn dos Santos, Cassia, Zamazal, Ondrej   +17 more
core   +6 more sources

Results of the ontology alignment evaluation initiative 2017 [PDF]

, 2016
Ontology matching consists of finding correspondences between semantically related entities of different ontologies. The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases.
Achichi, M., Cheatham, M., Dragisic, Z., Euzenat, J., Faria, D., Ferrara, A., Flouris, G., Fundulaki, I., Harrow, I., Ivanova, V., Jimenez-Ruiz, E., Kolthoff, K., Kuss, E., Lambrix, P., Leopold, H., Li, H., Meilicke, C., Mohammadi, M., Montanelli, S., Pesquita, C., Saveta, T., Shvaiko, P., Splendiani, A., Stuckenschmidt, H., Thiéblin, E., Todorov, K., Trojahn, C., Zamazal, O.   +27 more
core   +15 more sources

Matching sensor ontologies with unsupervised neural network with competitive learning [PDF]

PeerJ Computer Science, 2021
Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT).
Xingsi Xue, Haolin Wang, Wenyu Liu
doaj   +2 more sources

FOntCell: Fusion of Ontologies of Cells

Frontiers in Cell and Developmental Biology, 2021
High-throughput cell-data technologies such as single-cell RNA-seq create a demand for algorithms for automatic cell classification and characterization. There exist several cell classification ontologies with complementary information.
Javier Cabau-Laporta, Alex M. Ascensión, Mikel Arrospide-Elgarresta, Daniela Gerovska, Daniela Gerovska, Marcos J. Araúzo-Bravo, Marcos J. Araúzo-Bravo, Marcos J. Araúzo-Bravo, Marcos J. Araúzo-Bravo, Marcos J. Araúzo-Bravo, Marcos J. Araúzo-Bravo   +10 more
doaj   +1 more source

Results of the ontology alignment evaluation initiative 2020 [PDF]

https://ceur-ws.org/Vol-2788/oaei20_paper0.pdf, 2020
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus).
Pour, Mina Abd Nikooie, Algergawy, Alsayed, Amini, Reihaneh, Faria, Daniel, Fundulaki, Irini, Harrow, Ian, Hertling, Sven, Jiménez-Ruiz, Ernesto, Jonquet, Clement, Karam, Naouel, Khiat, Abderrahmane, Laadhar, Amir, Lambrix, Patrick, Li, Huanyu, Li, Ying, Hitzler, Pascal, Paulheim, Heiko, Pesquita, Catia, Saveta, Tzanina, Shvaiko, Pavel, Splendiani, Andrea, Thiéblin, Élodie, Trojahn, Cassia, Vataščinová, Jana, Yaman, Beyza, Zamazal, Ondřej, Zhou, Lu   +26 more
openaire   +5 more sources

Automatic background knowledge selection for matching biomedical ontologies. [PDF]

PLoS ONE, 2014
Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains ...
Daniel Faria, Catia Pesquita, Emanuel Santos, Isabel F Cruz, Francisco M Couto   +4 more
doaj   +1 more source

Solving Ontology Meta-Matching Problem Through an Evolutionary Algorithm With Approximate Evaluation Indicators and Adaptive Selection Pressure

IEEE Access, 2021
Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication.
Qing Lv, Chengcai Jiang, He Li
doaj   +1 more source