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.
A Novel Method to Associate Sensor Data with Domain Ontology, International Journal of Data Science and Analysis.
Vol. 5, No. 4,
2019, pp. 52-60.
Wang, J., Zhang, Z., Li, B., Lee, S., & Sherratt, R. S. An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE transactions on consumer electronics. Vol. 60, No. 1, 2014, pp. 23-29.
Liu, J., Li, Y., Tian, X., Sangaiah, A. K., & Wang, J. Towards Semantic Sensor Data: An Ontology Approach. Sensors. Vol. 19, No. 5, 2019, pp. 1193.
Liu, W., Chen, X., Jeon, B., Chen, L., & Chen, B. Influence maximization on signed networks under independent cascade model. Applied Intelligence. 2018, pp. 1-17.
Liu, J., Song, J. J., Kong, L., Kim, J. U., & Wang, J. A Novel Parallel Method for Denoising and Deduplicating Mass Web Documents. Journal of Internet Technology. Vol. 17, No. 5, 2016, pp. 889-896.
Hooi, Y. K., Hassan, M. F., & Shariff, A. M. A survey on ontology mapping techniques. Lecture Notes in Electrical Engineering. Vol. 279, No. 1, 2014, pp. 15-33.
Ma, Z., Zhang, F., Yan, L., & Cheng, J. Fuzzy semantic web ontology mapping. Studies in Fuzziness & Soft Computing. Vol. 306, 2014, pp. 157-180.
Trab, S., Bajic, E., Zouinkhi, A., Abdelkrim, M. N., & Chekir, H. RFID IoT-enabled warehouse for safety management using product class-based storage and potential fields methods. International Journal of Embedded Systems. Vol. 10, No. 1, 2018, pp. 71-88.
Vijayalakshmi, S. R., & Muruganand, S. Different soft computing algorithms used in fire sensor node of wireless sensor network integrated with IoT. International Journal of Embedded Systems. Vol. 9, No. 4, 2017, pp. 310-320.
Schrickte, L. F., Montez, C. B., Oliveira, R. S. D., & Pinto, A. S. R. Design and implementation of a 6LoWPAN gateway for wireless sensor networks integration with the internet of things. International Journal of Embedded Systems. Vol. 8, No. 5-6, 2016, pp. 380-390.
Huang, V., & Javed, M. K. Semantic sensor information description and processing. In 2008 Second International Conference on Sensor Technologies and Applications. 2008, pp. 456-461.
Ni, L. M., Zhu, Y., Ma, J., Li, M., Luo, Q., Liu, Y.,... & Yang, Q. Semantic sensor net: An extensible framework. In International Conference on Networking and Mobile Computing. 2005, pp. 1144-1153.
Liu, J., Zhou, M., Lin, L., Kim, H. J., & Wang, J. Rank web documents based on multi-domain ontology. Journal of Ambient Intelligence and Humanized Computing. 2017, pp. 1-10.
Kim, J. H., Kwon, H., Kim, D. H., Kwak, H. Y., & Lee, S. J. Building a service-oriented ontology for wireless sensor networks. In Seventh IEEE/ACIS International Conference on Computer and Information Science. 2008, pp. 649-654.
Atanasov, I., Nikolov, A., & Pencheva, E. An approach to transform Internet of Things data into knowledge. International Journal of Embedded Systems. Vol. 9, No. 5, 2017, pp. 401-412.
Compton, M., Henson, C. A., Neuhaus, H., Lefort, L., & Sheth, A. P. A Survey of the Semantic Specification of Sensors. International Conference on Semantic Sensor Networks, CEUR-WS. org. 2009, pp. 17-32.
Zong-Hao, L. I., Chun-He, X., & Cheng, Z. Research of the sensor network information sharing based on ontology. Science & Technology Vision. No. 29, 2013, pp. 5-6.
Chen, H., Ning-Jing, H. U., & Song, Y. A research on ontology mapping frame. Journal of Hunan University of Science & Engineering. Vol. 30, No. 12, 2009, pp. 58-62.
Fan, W., & Jian, C. Research on multi-strategy ontology mapping based on conceptual similarity computing. Computer Technology and Development. No. 4, 2015, pp. 38-42.
Ehrig, M., & Sure, Y. Ontology mapping–an integrated approach. In European Semantic Web Symposium, Springer, Berlin, Heidelberg. Vol. 3053, 2004, pp. 76-91.
Li, H., & Su, L. Comprehensive method of computing ontology similarity based on association rules. Jisuanji Yingyong/ Journal of Computer Applications. Vol. 32, No. 9, 2012, pp. 2472-2475.
Li, J., Tang, J., Li, Y., & Luo, Q. RiMOM: A dynamic multistrategy ontology alignment framework. IEEE Transactions on Knowledge and data Engineering. Vol. 21, No. 8, 2009, pp. 1218-1232.
Wang, P., Zhou, Y., & Xu, B. Matching large ontologies based on reduction anchors. Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, pp. 2343-2348.
Faria, D., Pesquita, C., Santos, E., Palmonari, M., Cruz, I. F., & Couto, F. M. The agreementmakerlight ontology matching system. In OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. 2013, pp. 527-541.
Prasad, S., Peng, Y., & Finin, T. A tool for mapping between two ontologies using explicit information. In AAMAS 2002 Workshop on Ontologies and Agent Systems, Bologna, Italy. 2002.
Doan, A., Madhavan, J., Domingos, P., & Halevy, A. Learning to map between ontologies on the semantic web. In Proceedings of the 11th international conference on World Wide Web, ACM, 2002, pp. 662-673.
Chuncheng, X., Zhifang, S., Weidong, Z., & University, P. On mapping between hownet and ccd. Journal of Chinese Information Processing. Vol. 29, No. 3, 2015, pp. 44-51.
Levenshtein, V. I. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady. Vol. 10, No. 8, 1966, pp. 707-710.
Stoilos, G., Stamou, G., & Kollias, S. A string metric for ontology alignment. In International Semantic Web Conference, Springer, Berlin, Heidelberg. 2005, pp. 624-637.
Liu, J., Lin, L., Ren, H., Gu, M., Wang, J., Youn, G., & Kim, J. U. Building neural network language model with POS-based negative sampling and stochastic conjugate gradient descent. Soft Computing. Vol. 22, 2018, pp. 6705-6717.
Sekine, S., Sudo, K., & Ogino, T. Statistical matching of two ontologies. SIGLEX99: Standardizing Lexical Resources. 1999, pp. 69-73.
Budanitsky, A., & Hirst, G. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on WordNet and other lexical resources. Vol. 2, 2001, pp. 2-2.
Hai-Tao, J., & Lin, Z. Integrated similarity calculation method based on domain ontology mapping. Modern Computer. No. 14, 2017, pp. 34-39.
Isaac, A., Van Der Meij, L., Schlobach, S., & Wang, S. An empirical study of instance-based ontology matching. In The Semantic Web, Springer, Berlin, Heidelberg, 2007, pp. 253-266.
Kumar, S. K., & Harding, J. A. Ontology mapping using description logic and bridging axioms. Computers in Industry. Vol. 64, No. 1, 2013, pp. 19-28.
Wang, M., Wang, J., Guo, L., & Harn, L. Inverted XML Access Control Model Based on Ontology Semantic Dependency. CMC: Computers, Materials & Continua. Vol. 55, No. 3, 2018, pp. 465-482.
Xiong, Z., Shen, Q., Wang, Y., & Zhu, C. Paragraph Vector Representation Based on Word to Vector and CNN Learning. CMC: Computers, Materials & Continua. Vol. 55, No. 2, 2018, pp. 213-227.
Wang, S., Zhang, L., Zhang, Y., Sun, J., Pang, C., Tian, G., & Cao, N. Natural Language Semantic Construction Based on Cloud Database. CMC: Computers, Materials & Continua. Vol. 57, No. 3, 2018, pp. 603-619.
Pinkel, C., Binnig, C., Jiménez-Ruiz, E., Kharlamov, E., May, W., Nikolov, A.,... & Heupel, C. RODI: Benchmarking relational-to-ontology mapping generation quality. Semantic Web. Vol. 9, No. 1, 2018, pp. 25-52.
Wang, R., Wang, L., Liu, L., Chen, G., & Wang, Q. Combination of the Improved Method for Ontology Mapping. Physics Procedia. Vol. 25, 2012, pp. 2167-2172.
Bock, J., & Hettenhausen, J. Discrete particle swarm optimisation for ontology alignment. Information Sciences. Vol. 192, 2012, pp. 152-173.
Zhang, L., Yin, C. Y., & Chen, J. Chinese word similarity computing based on semantic tree. Journal of Chinese Information Processing. Vol. 24, No. 6, 2010, pp. 23-30.
Compton, M., Barnaghi, P., Bermudez, L., GarcíA-Castro, R., Corcho, O., Cox, S.,... & Huang, V. The SSN ontology of the W3C semantic sensor network incubator group. Web semantics: science, services and agents on the World Wide Web. Vol. 17, No. 4, 2012, pp. 25-32.