Zhang, Lu2025-09-242025-09-242017[1] Sinha, A. , Shen, Z. , Song, Y. , Ma, H. , Eide, D., Hsu, B.J., & Wang, K. (2015). An Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW ’15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839 [2] Wang,Y., Qian,L., Xie, J., Chang, Z., Kong, B.(2019). Building Knowledge Graph with Sci-Tech Big Data. Data Analysis and Knowledge Discovery, DOI: 10.11925/infotech.2096-3467.2018.1354. [3] McCue, J., Chiang, K., Lowe, B., Caruso, B., Corson-Rikert, J., Devare, M.(2007). VIVO: Connecting people, creating a virtual life sciences community. D-Lib Magazine, ISSN 1082-9873, Vol. 13, Nº. 7-8, 2007. 13. 10.1045/july2007-devare. [4] Chen, Q., Cao, J., Chen, R.(2019). Research and Practices from the Thesaurus to Knowledge Graph[J]. Agricultural Library and Information, 31(1): 44-53. [5] Liu, J. , Shang, J. , Wang, C. , Ren, X. , & Han, J. (2015). Mining Quality Phrases from Massive Text Corpora. Acm Sigmod International Conference on Management of Data. Proc ACM SIGMOD Int Conf Manag Data. [6] Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics - Volume 2 (COLING '92), Vol. 2. Association for Computational Linguistics, Stroudsburg, PA, USA, 539-545. [7] Snow, R., Jurafsky, D., and Ng, A. Y. (2005). Learning syntactic patterns for automatic hypernym discovery. In Saul, L. K., Weiss, Y., and Bottou, L. (Eds.), NIPS 17, pp. 1297–1304. MIT Press. [8] Ji, L., Wang, Y.J., Shi, B., Zhang, D.W., Wang, Z.Y., & Yan, J.(2019). Microsoft concept graph: Mining semantic concepts for short text understanding. Data Intelligence. pp. 1-33. doi: 10.1162/dint_a_00013https://repository.ifla.org/handle/20.500.14598/6716Libraries have large amount of credible knowledge. But unfortunately, advanced Internet search tools and knowledge graphs cannot fully cover the valuable library collections. Using knowledge graph to describe collections can optimize knowledge services in several aspects. Specifically, the application of knowledge graph can help library enhance retrieval efficiency, integrate various resources and improve reference services. In order achieve these improvements, National Science Library of Chinese Academy of Sciences tries to build a knowledge graph connecting all the collections and open resources. The work of academic knowledge graph has been completed. However, in order to reveal the semantic connection of resources, a concept knowledge graph still needs to be working on, which is the focus of this research. This study proposes a process of constructing concept knowledge graph and discusses possible applications of knowledge graph in library.enAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Articlehttps://2019.ifla.org/conference-programme/satellite-meetings/open accessAcademic knowledge graphConcept knowledge graphLibrary collections