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Building thesaurus-based knowledge graph based on schema layer

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Abstract

Google proposed the concept of knowledge graph to improve the quality of searching results in 2012, so the knowledge graph brought a hot topic in the field of academic and industry. The knowledge graph can effectively improve the searching quality and the accuracy of Q & A system, which is a hot issue. With the help of agricultural experts, this paper based on the Agricultural Thesaurus, determines the rules for judging whether a thesaurus is a concept or an entity, establishes maps from Agricultural Thesaurus to the agricultural knowledge graph schema layer and the data layer. Therefore, the large-scale automatic building is realized from the Agricultural Thesaurus to the agricultural knowledge graph. In order to effectively manage and utilize triples, this paper proposes a mathematical model for the management of triples with the RDF-based triple storage pattern, which lays the solid foundation for the semantic-based agricultural information retrieving and the construction of the Q & A system.

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References

  1. Haofen, W.: Large scale knowledge graph technology. Commun. CCF 10(3), 64–68 (2014) (in chinese)

  2. Bollacker, K., Evans, C., Paritosh, P., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of data, pp. 1247–1250. ACM (2008)

  3. Lehmann, J., Isele, R., Jakob, M., et al.: DBpedia—a Large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web. J. 6(2), 167–195 (2014)

  4. Hoffart, J., Suchanek, F.M., Berberich, K., et al.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. 194, 28–61 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  5. Suchanek, F.M., Kasneci, G., Weikum, G., et al.: Yago: a core of semantic knowledge. Hierarchically Organized Syst. Theory Pract. 272(2), 181–221 (2007)

  6. Xu, Z., Zhang, H., Hu, C., Mei, L., Xuan, J., Raymond Choo, K.-K., Sugumaran, V., Zhu, Y.: Building knowledge base of urban emergency events based on crowd sourcing of social media. Concurrency Comput. Pract. Exp. 28(15), 4038–4052 (2016)

  7. Xu, Z., Wei, X., Liu, Y., Mei, L., Hu, C., Raymond Choo, K.-K., Zhu, Y., Sugumaran, V.: Building the search pattern of web users using conceptual semantic space model. IJWGS 12(3), 328–347 (2016)

  8. Xu, Z., Chen, H.: The semantic analysis of knowledge map for the traffic violations from the surveillance video big data. Comput. Syst. Sci. Eng. 30(5) (2015)

  9. Lauser, B., Sini, M.: From AGROVOC to the agricultural ontology service/concept server: an OWL model for creating ontologies in the agricultural domain. In: Dublin Core Conference Proceedings (2006)

  10. Soergel, D., Lauser, B., Liang, A.C., et al.: Reengineering thesauri for new applications: the AGROVOC example. J. Digital Inf. 4(4) (2004)

  11. Jing, T.: Research on thesaurus transfer into the ontology. Inf. Stud. Theory Appl. 27(6), 642–645 (2004)

    Google Scholar 

  12. Assem, M.V., Menken, M.R., Schreiber, G., et al.: A method for converting thesauri to RDF/OWL. Lect. Notes Comput. Sci. 3298, 17–31 (2004)

    Article  Google Scholar 

  13. Zhi-an, Y., Yue, W.: Thesaurus-based oil field ontology construction method. Comput. Syst. Appl. 24(9), 91–96 (2015)

    Google Scholar 

  14. Chunyan, L.: Research on SKOS-Based Thesaurus Transfer into the Ontology in the Semantic Web Environment. Jilin University (2006)

  15. Yi, L., Bo, Z.: Construction and application of government information ontology based on government thesaurus. Comput. Eng. Des. 31(3), 521–524 (2010)

    Google Scholar 

  16. Jianjun, G.: Method research on Chinese medical ancient literature ontology modeling. China Academy of Chinese Medical Sciences (2006)

  17. Aimin, T., Qin, Z., Jing, F.: Thesaurus-based domain ontology construction research. New Technol. Library Inf. Serv. 21(4), 1–5 (2005)

    Google Scholar 

  18. Jianwu, X., Miao, G., Tuo, W.: Research on SKOS-based defense science and technology thesaurus transfer into the ontology. J. China Soc. Sci. Technical Inf. 28(3), 310–317 (2011)

  19. Yun, X., Dongyi, Y., Wende, Z.: Construction research on domain ontology based on the “China Classified Thesaurus”. J. Intell. 26(3), 15–18 (2007)

    Google Scholar 

  20. Xinhong, Z., Zhong, M., Yin, J., et al.: Research on joint construction and sharing system research based on chinese thesaurus ontology. J. China Soc. Sci. Technical Inf. 27(3), 386–394 (2008)

    Google Scholar 

  21. Xinhong, Z.: Chinese thesaurus ontology: fusion thesaurus and ontology. New Technol. Library Inf. Serv. 1, 34–43 (2009)

    Google Scholar 

  22. Ministry of Agriculture Information Institute (1994) Agricultural Thesaurus. China Agriculture Press, Beijing (1994)

  23. Chunpei, H., Sijing, L.: Prospects and development of agricultural thesaurus and ontology in China. J. Library Inf. Sci. Agric. 4, 16–19 (2003)

    Google Scholar 

  24. Chun, C.: Construction and transformation of agricultural information management ontology. Chinese Academy of Agricultural Sciences (2004)

  25. Guojian, X.: Research and implementation of agricultural thesaurus transfer into the ontology system. Chinese Academy of Agricultural Sciences (2008)

  26. Guojian, X., Ruixue, Z., Liang, Z., et al.: Transformation and application of SKOS based on Agricultural thesaurus. J. Library Inf. Sci. Agric. 10, 16–20 (2012)

    Google Scholar 

  27. Guojian, X., Ruixue, Z., Yuantao, K., et al.: Research and practice on agricultural thesaurus linked data. J. Library Inf. Sci. Agric. 29(11), 8–14 (2013)

    Google Scholar 

  28. Qian, S.: Research on domain ontology modeling method based on thesaurus. Shandong University (2007)

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Acknowledgements

Our work has been fully supported by the National Science and Technology Funding Program (2012BAD35807), the Hunan Province’s Key Science and Technology Project (2016NK2118), as well as the Hunan Education Authority’s Science Research Project (13C389).

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Authors and Affiliations

  1. College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, Hunan Province, China

    Bo Qiao, Kui Fang, Yiming Chen & Xinghui Zhu

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  1. Bo Qiao
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  2. Kui Fang
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  3. Yiming Chen
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  4. Xinghui Zhu
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Correspondence to Kui Fang.

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Qiao, B., Fang, K., Chen, Y. et al. Building thesaurus-based knowledge graph based on schema layer. Cluster Comput 20, 81–91 (2017). https://doi.org/10.1007/s10586-016-0725-z

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