Keywords
University digital library
Document retrieval method
Semantic similarity
Submitted : 2021-11-23
Accepted : 2021-12-08
Published : 2021-12-23
Abstract
With the development of big data, all walks of life in society have begun to venture into big data to serve their own enterprises and departments. Big data has been embraced by university digital libraries. The most cumbersome work for the management of university libraries is document retrieval. This article uses Hadoop algorithm to extract semantic keywords and then calculates semantic similarity based on the literature retrieval keyword calculation process. The fast-matching method is used to determine the weight of each keyword, so as to ensure an efficient and accurate document retrieval in digital libraries, thus completing the design of the document retrieval method for university digital libraries based on Hadoop technology.
References
Gong J, Yang H, Wen H, 2021, The Development and Realization of the Video Service Platform of Small and Medium-Sized University Libraries: Taking Nanchang Hangkong University “Live Library Network Service Platform” as an Example. China Education Information, 2021(07): 86-91.
Shan Z, Shao B, 2021, Research on the Design and Evaluation of University Library Business Process Reorganization Based on the New Generation Service Platform. Research in Library Science, 2021(06): 27-35.
Chen L, Huang J, Wang R, 2018, Overview of Hadoop Big Data Platform Security Issues and Solutions. Computer System Applications, 27(01): 1-9.
Verma A, Pedrosa L, Korupolu M, et al., 2015, Proceedings of the Tenth European Conference on Computer Systems, April 21-24, 2015: Large-Scale Cluster Management at Google with Borg. EuroSys, Bordeaux, France, 18.
Ghemawat S, Gobioff H, Leung ST, 2003, The Google File System. ACM SIGOPS Operating Systems Review, 37(5): 29-43. DOI: 10.1145/1165389
Dean J, Ghemawat S, 2010, MapReduce: A Flexible Data Processing Tool. Communications of the ACM, 53(1): 72 -77. DOI: 10.1145/1629175
Burrows M, 2006, Proceedings of the 7th Symposium on Operating Systems Design and Implementation, November, 2006: The Chubby Lock Service for Loosely-Coupled Distributed Systems. OSDI, Berkeley, CA, USA, 335-350.
Shi S, 2017, Information Retrieval and Network Resource Utilization of Books and Documents in College Libraries. Exploration of Higher Education, 2017(S1): 139-140.
Zhang Q, 2021, Design of University Library Service Platform Based on Hadoop. Science and Technology Innovation, 2021(23): 83-84.
Zhang X, Zhang Y, Yang F, 2020, Research and Design of Document Retrieval Method for University Digital Library Based on Hadoop Technology. Microcomputer Applications, 36(07): 11-13, 23.
Liu F, 2017, Research on Reading Promotion of University Libraries Based on 4I Marketing Principles. Library Work and Research, 1(9): 36-39.
Han Z, Luo R, 2017, Experimental Research on the Influence of Academic Users’ Emotional Control and Mental Models on Information Retrieval Performance. Information Theory and Practice, 40(1): 59-64.
Zhou D, Zhao W, 2017, Research on Result Reordering in Personalized Cross-Language Information Retrieval. Computer Engineering and Science, 39(10): 1922-1929.