Research on Knowledge Retrieval of Red Documents under Integrated Human-Machine Intelligence

Abstract

Abstract:

The retrieval of red documents is an important pathway for advancing the construction anddeep utilization of red digital resources. At present the red document-based retrieval mainly relies onmetadata in which the query formulation strategies and navigation design mechanisms fail to adequatelymeet diverse user needs. In particular existing approaches lack in-depth mining and content-levelpresentation. To address the problems above this study proposes a user-centered knowledge retrievalsolution for red documents which integrates both the functional features of retrieval systems and theunique characteristics of red documents. The model incorporates cutting-edge human-machine intelligencetechnology into the current metadata-based retrieval framework. Specifically it enriches the retrievalscope through entity-level knowledge extraction from document content and introduces two queryexpansion modules namely a 􀆵 semantic expansion module and a 􀆵 human-AI interaction module .These modules provide users with expanded query terms at multiple levels of knowledge representation.Accordingly we developed a knowledge retrieval prototype system for red documents and conductedmultiple user experiments for evaluation. The results show that compared with existing metadata-drivenretrieval platforms our proposed system has more advantages in functional richness and user satisfaction. In addition the two newly proposed query expansion modules have their own distinct characteristics andapplicability to meet different types of search needs. These findings provide certain technical referencesfor the transformation and development of red document retrieval services towards a more knowledgeableand intelligent direction.

Key words: Integrated human-machine intelligence, Red literature, Knowledge retrieval, Query expansion

Yan Chengxi1 2 Liu Yuenan1 2 Yu Min1 Zhang Yiming2 3（1 School of Information Resource Management Renmin University of China 2 Digital HumanitiesCenter Renmin University of China 3 School of History Renmin University of China）. Research on Knowledge Retrieval of Red Documents under Integrated Human-Machine Intelligence[J]. Libraly Journal, 2025, 44(415): 15-27.

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