Matching sensor ontologies with unsupervised neural network with competitive learning
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). However, due to the subjectivity of the ontology building process, sensor ontologies might be de...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-763.pdf |
| _version_ | 1818398373438291968 |
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| author | Xingsi Xue Haolin Wang Wenyu Liu |
| author_facet | Xingsi Xue Haolin Wang Wenyu Liu |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | 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). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies. |
| first_indexed | 2024-12-14T07:03:45Z |
| format | Article |
| id | doaj.art-fac2aca02dcf4cc4b81aa96734f5c603 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-14T07:03:45Z |
| publishDate | 2021-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-fac2aca02dcf4cc4b81aa96734f5c6032022-12-21T23:12:20ZengPeerJ Inc.PeerJ Computer Science2376-59922021-11-017e76310.7717/peerj-cs.763Matching sensor ontologies with unsupervised neural network with competitive learningXingsi Xue0Haolin Wang1Wenyu Liu2Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, ChinaIntelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, ChinaIntelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, ChinaSensor 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). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.https://peerj.com/articles/cs-763.pdfArtificial intelligence of thingsSensor ontology matchingUnsupervised neural networkCompetitive learning |
| spellingShingle | Xingsi Xue Haolin Wang Wenyu Liu Matching sensor ontologies with unsupervised neural network with competitive learning PeerJ Computer Science Artificial intelligence of things Sensor ontology matching Unsupervised neural network Competitive learning |
| title | Matching sensor ontologies with unsupervised neural network with competitive learning |
| title_full | Matching sensor ontologies with unsupervised neural network with competitive learning |
| title_fullStr | Matching sensor ontologies with unsupervised neural network with competitive learning |
| title_full_unstemmed | Matching sensor ontologies with unsupervised neural network with competitive learning |
| title_short | Matching sensor ontologies with unsupervised neural network with competitive learning |
| title_sort | matching sensor ontologies with unsupervised neural network with competitive learning |
| topic | Artificial intelligence of things Sensor ontology matching Unsupervised neural network Competitive learning |
| url | https://peerj.com/articles/cs-763.pdf |
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