An information retrieval method, medium and device

By using a material relationship knowledge graph for multi-dimensional drug information retrieval, integrating multiple types of nodes and combining them with visual output, the problem of inaccurate and inefficient drug information retrieval in existing technologies is solved, and comprehensive, accurate and efficient retrieval of multi-dimensional information is achieved.

CN121979929BActive Publication Date: 2026-07-24YUYANG ZHISHU (BEIJING) TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
YUYANG ZHISHU (BEIJING) TECHNOLOGY CO LTD
Filing Date
2026-01-28
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing drug information retrieval methods cannot meet the needs of multi-dimensional cross-search, lack visualization of drugs, genes, pathways, diseases and treatment plans, and do not fully integrate clinical diagnosis and treatment guidelines, resulting in inaccurate search results and low efficiency.

Method used

It employs a material relationship knowledge graph for multi-dimensional keyword retrieval, integrating preset material nodes, target nodes, gene nodes, pathway nodes, disease nodes, and treatment plan nodes. Through association expansion, it generates associated sub-graphs, and combines them with structured material information tables and visual output, integrating authoritative evidence such as clinical diagnosis and treatment guidelines.

Benefits of technology

It enables comprehensive retrieval of multi-dimensional information, improves the accuracy and reliability of retrieval results, enhances information acquisition efficiency and user experience, and meets the needs of multiple scenarios in clinical diagnosis and treatment and scientific research.

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Abstract

The present application relates to the technical field of information retrieval, and in particular to an information retrieval method, medium and device, which supports the retrieval of three types of keywords and integrates multiple types of nodes in a graph, pre-constructs a material relationship knowledge graph containing multiple-dimensional nodes and associated relationships, combines the visual output of the material information table and the associated sub-graph, and intuitively displays the logical chain between the drug and each associated element, so that the user's associated query requirements in multiple scenarios can be met without multiple searches across platforms; by integrating authoritative data during the graph construction process, the association between the drug, treatment plan and disease has clinical rationality and authority, and by extracting the associated sub-graph through association expansion, false associations and irrelevant information are effectively filtered, and the accuracy and reliability of the search results are improved; by outputting the first and second search result pages in stages, the user's demand for quickly screening the target material is met, and detailed information is also provided, balancing the search efficiency and information depth.
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