Abstract:

[Objective] Due to the specialization of terminology, complex knowledge associations and low retrieval efficiency in the current technology project data resources, the traditional retrieval mode is difficult to meet the needs of multimodal scientific data association retrieval, resulting in the difficulty of knowledge sharing and integrated application of existing technology project data resources. [Methods] To address the above problems, a multimodal scientific data retrieval method based on summary-assisted cognitive enhancement is proposed to promote open sharing of scientific and technological resources. Firstly, a multimodal scientific data semantic representation model based on large models is constructed, which combines professional domain knowledge with structural guidance mechanisms to generate structured summaries containing key knowledge. Subsequently, based on the knowledge of keywords and description information provided by structured abstracts, a multimodal scientific data retrieval model is constructed with directed thought chain multiplex guidance. By combining cognitive cueing with multi-step dynamic reasoning, this model enables multi-dimensional correlation analysis of scientific data and improves retrieval performance. [Results] The results show that, compared with the existing Modular RAG framework, the proposed method improves accuracy and Rouge-n index on the test set by 4.65% and 3.18% respectively. [Conclusions] These results verify the applicability of the proposed method to scientific data retrieval involving multimodal data and complex knowledge associations.

Key words: multi-format scientific data retrieval, multi-perspective semantic association, structured summary, cognitive enhancement, multi-channel guidance of thinking chain