Author(s)

Yang Lei ¹

Affiliation(s)

¹ Yunnan Open University, Kunming, China

Corresponding Author

Yang Lei

ABSTRACT

Research management in universities involves massive multi source heterogeneous data scattered across diverse systems and formats. Conventional relational database management modes can hardly satisfy the requirements of cross domain associative query and intelligent question answering. To tackle this practical challenge, this paper takes university research management as the application scenario and proposes a scheme for constructing a research management knowledge graph and its question answering application oriented to multi source heterogeneous data. This study systematically sorts out the heterogeneous features of data sources in research management, designs a knowledge graph construction workflow covering data access, ontology modeling, knowledge extraction, knowledge fusion and knowledge storage. Furthermore, a knowledge graph question answering framework integrating semantic parsing and graph query is built. Combined with retrieval augmented generation technology, interpretable intelligent question answering services are realized. Taking the qualification review for research applications at Yunnan Open University as the validation scenario, experimental results demonstrate that the proposed method can effectively integrate multi source data including title evaluation, project application and achievement recognition. It improves associative query efficiency and question answering accuracy, and provides a feasible technical path for the intelligent transformation of university research management.

KEYWORDS

Knowledge graph; Multi‑source heterogeneous data; Research management; Intelligent question‑answering; Retrieval‑augmented generation

CITE THIS PAPER

Yang Lei. Construction and Question‑Answering Application of Research Management Knowledge Graph for Multi‑Source Heterogeneous Data — A Case Study of Research Application Qualification. Journal of Artificial Intelligence Practice (2026). Vol. 9, No. 2, 25-33. DOI: http://dx.doi.org/10.23977/jaip.2026.090203.

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