Abstract: The rapid expansion of data resources has led to a significant emphasis on the effective organization,discovery,and utilization of datasets within the domain of data management.Conventional approaches that rely on metadata matching or statistical retrieval often fail to adequately capture intricate semantic relationships,resulting in diminished accuracy and interpretability in the retrieval of datasets.In response to this challenge,this study proposes a methodology for the construction and retrieval of dataset knowledge graphs through the enhancement of metadata semantics,with the objective of augmenting the semantic retrieval capabilities of datasets.Initially,it standardizes dataset metadata in accordance with the W3C DCAT specification to establish a foundational knowledge graph that encompasses essential attributes such as titles,keywords,subject categories,and data items.Subsequently,to address the shortcomings associated with the semantic descriptions of metadata,it incorporates the Wikidata general knowledge graph to enrich entity semantics via cross-domain semantic expansion.In the retrieval phase,the BERT-BiLSTM-CRF model is employed to extract key entities from user queries and construct semantic relationship subgraphs.By integrating entity vector representations generated via Wikipedia2vec,it implements structured semantic retrieval ranking using cosine similarity calculations.Experiments conducted on the government open data platforms of Fuzhou and Shenzhen demonstrate that the proposed method achieves Top-10 hit rates of 97.92% and 98.25%,respectively－representing improvements of 8.92%~12.04% over traditional BM25 and 5.72%~8.96% over Word2Vec-enhanced methods.The results highlight that semantic enhancement via Wikidata and structured graph matching significantly boost retrieval accuracy by explicitly modeling entity relationships and enriching metadata semantics.This study provides a feasible technical solution for enhancing dataset discovery in scenarios such as open data platforms and research data management,showcasing the effectiveness of integrating semantic enrichment with knowledge graph structures.

Key words: Metadata semantic enhancement, Dataset knowledge graph, Knowledge graph construction, Semantic retrieval, Open data platforms