Results 41 to 50 of about 318 (125)

Matching biomedical ontologies through compact differential evolution algorithm

Systems Science & Control Engineering, 2019
Although biomedical ontologies have been widely used in the life science domain, the heterogeneous problem among biomedical ontologies hampers their inter-operability.
Xingsi Xue, Junfeng Chen
doaj   +1 more source

Instance-Based Ontology Matching For Open and Distance Learning Materials

International Review of Research in Open and Distributed Learning, 2017
The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content.
Sergio Cerón-Figueroa, Itzamá López-Yáñez, Yenny Villuendas-Rey, Oscar Camacho-Nieto, Mario Aldape-Pérez, Cornelio Yáñez-Márquez   +5 more
doaj   +1 more source

A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

Complex & Intelligent Systems, 2022
Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them.
Aifeng Geng, Qing Lv
doaj   +1 more source

Sensor Ontology Metamatching with Heterogeneity Measures

Wireless Communications and Mobile Computing, Volume 2020, Issue 1, 2020., 2020
The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts.
Xingsi Xue, Jiawei Lu, Chengcai Jiang, Yikun Huang, Feng-Jang Hwang   +4 more
wiley   +1 more source

Interactive Cross-Lingual Ontology Matching

IEEE Access, 2019
Recently, with the growing number of ontologies defined in different languages, to bridge the semantic gaps between them, it is necessary to identify the correspondences between their heterogeneous entities, so-called cross-lingual ontology matching. Due
Junfeng Chen, Xingsi Xue, Yikun Huang, Xuewu Zhang   +3 more
doaj   +1 more source

Semantic Integration of Sensor Knowledge on Artificial Internet of Things

Wireless Communications and Mobile Computing, Volume 2020, Issue 1, 2020., 2020
Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co‐operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome ...
Yikun Huang, Xingsi Xue, Chao Jiang, Pei-Wei Tsai   +3 more
wiley   +1 more source

MultiFarm: A benchmark for multilingual ontology matching

, 2012
In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between
Svátek, Vojtěch, Stuckenschmidt, H., Wang, Shenghui Shenghui, Wang, Shenghui, Meilicke, C., Meilicke, Christian, van Hage, W.R., Ribeiro de Azevedo, Ryan, García Castro, Raúl, Montiel Ponsoda, Elena, Azevedo, Ryan Ribeiro de, Šváb-Zamazal, Ondřej, Tamalin, A., Freitas, Fred, Trojahn, Cássia, Trojahn dos Santos, Cassia, García-Castro, Raúl, Montiel-Ponsoda, E., Montiel-Ponsoda, Elena, van Hage, Willem Robert, Ribeiro de Azevedo, R., Stuckenschmidt, Heiner, Wang, S., Freitas, F., Freitas, Frederico, Tamilin, Andrei, Svatek, V., Hage, Willem Robert van, Svab-Zamazal, O., Robert van Hage, Willem, García-Castro, R.   +30 more
core   +1 more source

Bayesian Evaluation and Comparison of Ontology Alignment Systems

IEEE Access, 2019
Ontology alignment systems are evaluated by various performance scores, which are usually computed by a ratio related directly to the frequency of the true positives.
Majid Mohammadi
doaj   +1 more source

Results of the Ontology Alignment Evaluation Initiative 2021

, 2022
62108The 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.
Jimenez-Ruiz, E., Michel, F., Roussey, C., Zamaza, O., Karam, N., Paulheim, H., Li, Y., Faria, D., Hertling, S., Fundulaki, I., Pesquita, C., Amini, R., Harrow, I., Splendiani, A., Vatascinova, J., Amardeilh, F., Yaman, B., Nasr, E., Pour, M.A.N., Portisch, J., Saveta, T., Fallatah, O., Laadhar, A., Huschka, M., Zhou, L., Shvaiko, P., Trojahn, C., Ibanescu, L., Algergawy, A., Lambrix, P., Li, H., Hitzler, P.   +31 more
core  

Results of the ontology alignment evaluation initiative 2018

, 2022
76116The 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 (from simple thesauri to expressive OWL ...
Ferrara, A., Jimenez-Ruiz, E., Schmidt, D., Karam, N., Saveta, T., Paulheim, H., Thieblin, E., Zamazal, O., Faria, D., Zhou, L., Hertling, S., Cheatham, M., Vatascinová, J., Shvaiko, P., Fundulaki, I., Khiat, A., Trojahn, C., Pesquita, C., Algergawy, A., Harrow, I., Splendiani, A., Lambrix, P., Li, H., Montanelli, S.   +23 more
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