Results 1 to 10 of about 3,434 (243)

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   +9 more sources

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   +8 more sources

Ontology Alignment Evaluation Initiative: Six Years of Experience [PDF]

Lecture Notes in Computer Science, 2011
In the area of semantic technologies, benchmarking and systematic evaluation is not yet as established as in other areas of computer science, e.g., information retrieval. In spite of successful attempts, more effort and experience are required in order to achieve such a level of maturity.
Cassia Trojahn, Christian Meilicke, Jérôme Euzenat   +2 more
exaly   +7 more sources

Results of the ontology alignment evaluation initiative 2022 [PDF]

, 2022
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. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants.
Pour, Mina Abd Nikooie, Algergawy, Alsayed, Buche, Patrice, Castro, Leyla Jael, Chen, Jiaoyan, Dong, Hang, Fallatah, Omaima, Faria, Daniel, Fundulaki, Irini, Hertling, Sven, He, Yuan, Horrocks, Ian, Huschka, Martin, Ibanescu, Liliana, Jiménez-Ruiz, Ernesto, Karam, Naouel, Laadhar, Amir, Lambrix, Patrick, Li, Huanyu, Li, Ying, Michel, Franck, Nasr, Engy, Paulheim, Heiko, Pesquita, Catia, Saveta, Tzanina, Shvaiko, Pavel, Trojahn, Cassia, Verhey, Chantelle, Wu, Mingfang, Yaman, Beyza, Zamazal, Ondrej, Zhou, Lu   +31 more
core   +18 more sources

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
core   +11 more sources

Using NSGA-III for optimising biomedical ontology alignment

CAAI Transactions on Intelligence Technology, 2019
To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts
Xingsi Xue, Jiawei Lu, Jiawei Lu, Junfeng Chen   +3 more
doaj   +3 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

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