<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a domain knowledge base construction platform for thematic intelligence services of library intelligence institutions. The platform proposes a keyword extraction algorithm applicable to the literature resources of library intelligence services based on a statistical model and N-Gram model combined with feature word information. The automatic summary algorithm based on statistics combines the information of sentence content and location to comprehensively evaluate the weights of candidate summary sentences and achieve automatic summary extraction. Based on the HDP topic model, a literature recommendation model oriented to the resource retrieval context is proposed, which can convert the literature feature similarity calculation to the topic similarity calculation with implied literature feature semantics from the perspective of semantic analysis and improve the accuracy of recommended literature. The technical feasibility of this system platform is verified through relevant data experiments. Regarding the accuracy of the literature recommendation, the platform of this paper and the traditional method are 86.36% and 75.16%, respectively, and the recall rate is 67.29% and 57.25%, respectively. The platform scored 3.85 on the comprehensive evaluation of library intelligence service, close to the excellent level. Therefore, the platform can realize various functions, from resource collection and organization to resource processing and utilization, with certain practical significance.</p>
</abstract>

This paper proposes a domain knowledge base construction platform for thematic intelligence services of library intelligence institutions. The platform proposes a keyword extraction algorithm applicable to the literature resources of library intelligence services based on a statistical model and N-Gram model combined with feature word information. The automatic summary algorithm based on statistics combines the information of sentence content and location to comprehensively evaluate the weights of candidate summary sentences and achieve automatic summary extraction. Based on the HDP topic model, a literature recommendation model oriented to the resource retrieval context is proposed, which can convert the literature feature similarity calculation to the topic similarity calculation with implied literature feature semantics from the perspective of semantic analysis and improve the accuracy of recommended literature. The technical feasibility of this system platform is verified through relevant data experiments. Regarding the accuracy of the literature recommendation, the platform of this paper and the traditional method are 86.36% and 75.16%, respectively, and the recall rate is 67.29% and 57.25%, respectively. The platform scored 3.85 on the comprehensive evaluation of library intelligence service, close to the excellent level. Therefore, the platform can realize various functions, from resource collection and organization to resource processing and utilization, with certain practical significance.

Keyword Extraction-based Library Intelligence Services: Challenges, Adaptations and Reinvention

Library of Linyi University, Linyi University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper proposes a domain knowledge base construction platform for thematic intelligence services of library intelligence institutions. The platform...

Keyword Extraction-based Library Intelligence Services: Challenges, Adaptations and Reinvention

Online veröffentlicht: 16. Sept. 2023

Seitenbereich: -

Eingereicht: 19. Nov. 2022

Akzeptiert: 27. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00330

Schlüsselwörter
Library intelligence services, N-Gram model, HDP model, Keyword extraction, Literature recommendation

© 2023 Ruixia Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keyword Extraction-based Library Intelligence Services: Challenges, Adaptations and Reinvention

Online veröffentlicht: 16. Sept. 2023

Seitenbereich: -

Eingereicht: 19. Nov. 2022

Akzeptiert: 27. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00330

SchlüsselwörterLibrary intelligence services, N-Gram model, HDP model, Keyword extraction, Literature recommendation

© 2023 Ruixia Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Library intelligence services, N-Gram model, HDP model, Keyword extraction, Literature recommendation