{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:49:34Z","timestamp":1701478174541},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>For the current museum small-scale knowledge map of historical relics needs to be constructed manually, this paper will introduce the relevant methods of deep learning to construct the knowledge map of historical relics, and conducts an in-depth study and analysis of the entity recognition model and algorithm in the construction of the knowledge map of relics, and improves the accuracy of model detection by improving and optimizing the model. In the process of entity recognition in this paper, the bi-directional long and short-term memory-conditional random field (Bi LSTM-CRF) and convolutional neural network (CNN) are combined to construct a network model for extracting entity information. In the comparison experiments, its data source is a self-built dataset based on historical artifact datasets. The experimental results show that the improved Bi LSTM-CNN-CRF model has a better entity recognition effect compared with the CRF, Bi LSTM, and Bi LSTM-CRF models, and it achieves an accuracy rate of up to 85.77% and an F-value of 84.49%.<\/jats:p>","DOI":"10.3233\/faia230893","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:57:26Z","timestamp":1701446246000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on the Construction Method of Historical Heritage Knowledge Map Based on Deep Learning"],"prefix":"10.3233","author":[{"given":"Shan","family":"Gui","sequence":"first","affiliation":[{"name":"Wuhan Qingchuan University, Wuhan 430204, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230893","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:57:27Z","timestamp":1701446247000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230893"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230893","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}