A Novel Method to Associate Sensor Data with Domain Ontology

Jin Liu; Yihe Yang; Shengjie Shang

doi:doi:10.11648/j.ijdsa.20190504.11

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A Novel Method to Associate Sensor Data with Domain Ontology

Jin Liu, Yihe Yang, Shengjie Shang

Published in International Journal of Data Science and Analysis (Volume 5, Issue 4)

Received: 6 July 2019 Accepted: 26 July 2019 Published: 16 August 2019

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Abstract

With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.

Published in	International Journal of Data Science and Analysis (Volume 5, Issue 4)
DOI	10.11648/j.ijdsa.20190504.11
Page(s)	52-60
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Ontology, Semantic Similarity, Structural Similarity, Sensor Data

References

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APA Style

Jin Liu, Yihe Yang, Shengjie Shang. (2019). A Novel Method to Associate Sensor Data with Domain Ontology. International Journal of Data Science and Analysis, 5(4), 52-60. https://doi.org/10.11648/j.ijdsa.20190504.11

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ACS Style

Jin Liu; Yihe Yang; Shengjie Shang. A Novel Method to Associate Sensor Data with Domain Ontology. Int. J. Data Sci. Anal. 2019, 5(4), 52-60. doi: 10.11648/j.ijdsa.20190504.11

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AMA Style

Jin Liu, Yihe Yang, Shengjie Shang. A Novel Method to Associate Sensor Data with Domain Ontology. Int J Data Sci Anal. 2019;5(4):52-60. doi: 10.11648/j.ijdsa.20190504.11

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@article{10.11648/j.ijdsa.20190504.11,
  author = {Jin Liu and Yihe Yang and Shengjie Shang},
  title = {A Novel Method to Associate Sensor Data with Domain Ontology},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {4},
  pages = {52-60},
  doi = {10.11648/j.ijdsa.20190504.11},
  url = {https://doi.org/10.11648/j.ijdsa.20190504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190504.11},
  abstract = {With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.},
 year = {2019}
}

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TY  - JOUR
T1  - A Novel Method to Associate Sensor Data with Domain Ontology
AU  - Jin Liu
AU  - Yihe Yang
AU  - Shengjie Shang
Y1  - 2019/08/16
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190504.11
DO  - 10.11648/j.ijdsa.20190504.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 52
EP  - 60
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190504.11
AB  - With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.
VL  - 5
IS  - 4
ER  -

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Author Information

Jin Liu

College of Information Engineering, Shanghai Maritime University, Shanghai, China
Yihe Yang

College of Information Engineering, Shanghai Maritime University, Shanghai, China
Shengjie Shang

College of Information Engineering, Shanghai Maritime University, Shanghai, China

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Plain Text BibTeX RIS

APA Style

Jin Liu, Yihe Yang, Shengjie Shang. (2019). A Novel Method to Associate Sensor Data with Domain Ontology. International Journal of Data Science and Analysis, 5(4), 52-60. https://doi.org/10.11648/j.ijdsa.20190504.11

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ACS Style

Jin Liu; Yihe Yang; Shengjie Shang. A Novel Method to Associate Sensor Data with Domain Ontology. Int. J. Data Sci. Anal. 2019, 5(4), 52-60. doi: 10.11648/j.ijdsa.20190504.11

Copy | Download

AMA Style

Jin Liu, Yihe Yang, Shengjie Shang. A Novel Method to Associate Sensor Data with Domain Ontology. Int J Data Sci Anal. 2019;5(4):52-60. doi: 10.11648/j.ijdsa.20190504.11

Copy | Download

@article{10.11648/j.ijdsa.20190504.11,
  author = {Jin Liu and Yihe Yang and Shengjie Shang},
  title = {A Novel Method to Associate Sensor Data with Domain Ontology},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {4},
  pages = {52-60},
  doi = {10.11648/j.ijdsa.20190504.11},
  url = {https://doi.org/10.11648/j.ijdsa.20190504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190504.11},
  abstract = {With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - A Novel Method to Associate Sensor Data with Domain Ontology
AU  - Jin Liu
AU  - Yihe Yang
AU  - Shengjie Shang
Y1  - 2019/08/16
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190504.11
DO  - 10.11648/j.ijdsa.20190504.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 52
EP  - 60
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190504.11
AB  - With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.
VL  - 5
IS  - 4
ER  -

Copy | Download