Research on the Evolution Method of Domain Ontology Based on DBpedia
Authors: 
Ziyu Liu, Lijuan YinAuthors Info & Claims
Pages 1735 - 1749
Abstract
Domain ontology is a model that regulates the core knowledge of domain description and semantic organization, which changes along with the change of domain knowledge. Automated or semi-automated evolution approaches are the hot researches in the dynamic update of domain ontology. In the relevant researches of domain ontology evolution, the most of them are based on the unstructured domain corpora. Chinese or English word segmentation tools are adopted to conduct the pattern matching so as to realize the evolution of ontology, which is relatively complicated. This paper puts forward the way of realizing ontology evolution based on the structured data. DBpedia is a comprehensive and huge structured data set extracted from Wikipedia, whose data structure can effectively connect with ontology data, so as to provide an feasible acquisition way of data in the semi-automatic evolution of domain ontology. This paper adopts the DBpedia structured data set as the evolution source of domain ontology, and puts forward the evolution methods of domain ontology based on the DBpedia. The main steps include DBpedia information extraction and optimization, access to evolutionary information, change operation of ontology and consistency checking. Taking the domain ontology of high-speed railway as an experimental object, this paper realizes the simultaneous evolution of Chinese and English domain ontology in it, which provides references to the evaluation of Chinese domain ontology based on DBpedia.
References
[1]
Yumeng, Ma., Fenghong, L., & Jinxia, H. (2015). Research on model framework for domain ontology of STKOS. Library and Information Service,59(3), 119---125.
[2]
Ruiz-Martinez, J. M., Valencia-Garcia, R., Fernandez-Breis, J. T., et al. (2011). Ontology learning from biomedical natural language documents using UMLS. Expert Systems with Applications,38, 12365---12378.
[3]
Serra, I., Girardi, R., & Novais, P. (2014). Evaluating techniques for learning non-taxonomic relationships of ontologies from text. Expert Systems with Applications,41, 5201---5211.
[4]
Lihong, C., Jing, M., Yizhan, W., Sheng, T., & Hao, X. (2011). Study of semiautomatic ontology evolution based on OWL. Journal of The China Society for Scientific and Technical Information,30(1), 56---60.
[5]
Zhang, X., Liu, X., Li, X., & Pan, D. (2017). MMKG: an approach to generate metallic materials knowledge graph based on DBpedia and Wikipedia. Computer Physics Communications,211, 98---112.
[6]
Ma, W., & Du, X. (2006). A study on domain ontology evolution. Library and Information Service,6(50), 71---75.
[7]
Lin, D., & Pantel, P. (2001). Induction of semantic classes from natural language text. In Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA (pp. 317---322).
[8]
Tong, X., Ye, Z., Xu, Y., Liu, S., & Li, L. (2015). A novel subpixel phase correlation method using singular value decomposition and unified random sample consensus. IEEE Transactions on Geoscience and Remote Sensing,53(8), 4143---4156.
[9]
Lan, Y., & Shi, C. (2006). A study on the method for automatic ontology acquisition based on language analysis technology. Library and Information Service,50(9), 22---25.
[10]
Lei, W., Kuanjiu, Z., & Peng, Q. (2010). Automatic domain ontology construction. Journal of The China Society for Scientific and Technical Information,29(1), 45---52.
[11]
Lastra-Diaz, J., & Garcia-Serrano, A. (2015). A new family of information content models with an experimental survey on WordNet. Knowledge-Based Systems,89(11), 509---526.
[12]
Suchanek, F. M., Kasneci, G., & Weikum, G. (2008). YAGO: a large ontology from Wikipedia and WordNet. Web Semantics: Science, Services and Agents on the World Wide Web,6(3), 203---217.
[13]
JMA Co. (2015). Filtered DBLP for time-series based link prediction, August 11, 2015. https://dl.dropboxusercontent.com/u/10080671/Resources/dblp.xml.
[14]
Färber, M., Ell, B., Menne, C., Rettinger, A., & Bartscherer, F. (2016). Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semantic Web,1, 1---5.
[15]
Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems,37(1), 186---195.
[16]
Zablith, F., Antoniou, G., Daquin, M., et al. (2014). Ontology evolution: a process centric survey. The Knowledge Engineering Review,30(1), 45---75.
[17]
Wang, W., & Stewart, K. (2015). Spatiotemporal and semantic information extraction from Web news reports about natural hazards. Computers, Environment and Urban Systems,50(5), 30---40.
[18]
Chen, Y., Lu, Q., Li, W., et al. (2010). An improve method for Chinese core ontology construction. Journal of Chinese Information Processing,24(1), 48---53.
[19]
Jia, W., & He, F. (2011). Research of chinese ontology learning based on HowNet. Computer Technology and Development,21(6), 77---80.
- Research on the Evolution Method of Domain Ontology Based on DBpedia
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Published: 01 September 2018
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