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Research on Latent Semantic Relationship Search Engine Based on Knowledge Graph

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Mobile Networks and Management (MONAMI 2023)

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Abstract

Knowledge graph is a large database composed of entities, relationships and attributes, which can provide rich semantic information for search engines. The potential semantic relation search engine based on Knowledge graph is a novel search engine. It obtains potential semantic relationships from the Knowledge graph, and then uses these potential semantic relationships to search for data sources such as web pages and documents. This paper first analyzes the characteristics of the Knowledge graph, then lists the construction process of the Knowledge graph based on WordNet, and finally proposes the potential semantic relationship search engine architecture based on the Knowledge graph.

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References

  1. Investigators at Nanyang Technological University Detail Findings in Neural Networks and Learning Systems (Brain-inspired Search Engine Assistant Based On Knowledge Graph). 2022(Jan.17), 13–14

    Google Scholar 

  2. Greg, R.: Notess. Search engine to knowledge engine? Online Search. 37(4), 61–63 (2013)

    Google Scholar 

  3. Mayank, K., Pedro, S.: Knowledge graph for social good: an entity-centric search engine for the human trafficking domain. IEEE Trans. Big Data 8(3), 592–606 (2022). https://doi.org/10.1109/TBDATA.2017.2763164

  4. Uyar, A., Aliyu, F.M.: Evaluating search features of Google Knowledge Graph and Bing Satori Entity types, list searches and query interfaces. Online Inf. Rev. 39(2), 197–213 (2015). https://doi.org/10.1108/OIR-10-2014-0257

  5. Asgari-Bidhendi, M., Hadian, A., Minaei-Bidgoli, B.: FarsBase: the Persian knowledge graph. Semant. Web 10(6), 1169–1196 (2019). https://doi.org/10.3233/SW-190369

  6. Du, Y., Li, C., Hu, Q., et al.: Ranking webpages using a path trust Knowledge graph. Neurocomputing 269(Dec.20), 58–72 (2017). https://doi.org/10.1016/j.neucom.2016.08.142

  7. MICROSOFT TECHNOLOGY LICENSING, LLC. Knowledge graph for conversational semantic search:US15664124. 2022-09-06

    Google Scholar 

  8. Ma, C., Zhang, B.: A New query recommendation method supporting exploratory search based on search goal shift graphs. IEEE Trans. Knowl. Data Eng. 30(11), 2024–2036 (2018). https://doi.org/10.1109/TKDE.2018.2815544

    Article  Google Scholar 

  9. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (2001)

    Google Scholar 

  10. Gruber, T.H.: A translation approach to portable ontology specifications. Knowl. Acquis. 2, 199–220 (1993)

    Article  Google Scholar 

  11. Han, L., Finin, T., Joshi, A.: GoRelations: an intuitive query system for DBpedia. Semant. Web 04, 674–693 (2012)

    Google Scholar 

  12. Li, Q., Yang, W., Ye, X., Ma, X.: Research on knowledge base of device test training system based on rough set data mining. In: Proceedings of the 2013 International Conference on Intelligent System, Applied Materials and Control Technology (GSAMCT 2013) (2013)

    Google Scholar 

  13. Bordes, A, Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the NIPS (2015)

    Google Scholar 

  14. Momtchev, V., Peychev, D., Primov, T.: Expanding the pathway and interaction knowledge in linked life data. In: Proceedings of International Semantic Web Challenge (2015)

    Google Scholar 

  15. Meng, Z.: Research on Construction of Course Knowledge Graph and Search Technology. Dissertation for Doctor Degree of Wuhan University (2016)

    Google Scholar 

  16. Xiangqian, L.: A method of searching entities based on wordnet noun network. Dissertation for Master Degree of Nanjing University (2015)

    Google Scholar 

  17. Yuncheng, G.: Research on Chinese-English-Mongolian Term Knowledge Graph of Computer Field Based on WordNet. Dissertation for Master Degree of Inner Mongolia Normal University (2021)

    Google Scholar 

  18. Berthold, M.R.: Towards bisociative knowledge discovery. Bisociative Knowledge Discovery. Springer-Verlag, Berlin, Heidelberg (2012)

    Google Scholar 

  19. Tom, H., Christian, B.: Linked Data. Morgan & Claypool., San Rafael (2011)

    Google Scholar 

  20. Berasaluce, S., Laurenço, C., Napoli, A., Niel, G.: An experiment on knowledge discovery in chemical databases. In: Boulicaut, J.F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) Knowledge Discovery in Databases: PKDD 2004. PKDD 2004. LNCS, vol. 3202. Springer, Berlin, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30116-5_7

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Author information

Authors and Affiliations

  1. Jiangxi Teachers College, Yingtan, 335000, Jiangxi, China

    Minqin Mao & Jielan Zhang

Authors
  1. Minqin Mao
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  2. Jielan Zhang
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Corresponding author

Correspondence to Minqin Mao .

Editor information

Editors and Affiliations

  1. The University of Electro-Communications, Tokyo, Japan

    Celimuge Wu

  2. VTT Technical Research Centre, Helsinki, Finland

    Xianfu Chen

  3. Xidian University, Xi'an, China

    Jie Feng

  4. Jiangxi University of Technology, Nanchang, China

    Zhen Wu

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Mao, M., Zhang, J. (2024). Research on Latent Semantic Relationship Search Engine Based on Knowledge Graph. In: Wu, C., Chen, X., Feng, J., Wu, Z. (eds) Mobile Networks and Management. MONAMI 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-55471-1_20

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  • DOI: https://doi.org/10.1007/978-3-031-55471-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-55470-4

  • Online ISBN: 978-3-031-55471-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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