Semi-automatic construction of heterogeneous data schema based on structure and context-aware recommendation

Customizing the structure and format of scientific data facilitates the publication of diverse and heterogeneous data. Many data publishing platforms empower users to create self-designed schemas, leading to schema proliferation and more intricate creation processes. To address these challenges, we present a semi-automatic method and system for constructing heterogeneous material data schemas based on structure and context-aware recommendation. We propose a schema fragment tree structure to represent data schemas with hierarchical relationships, transforming the recommendation into subtree matching. Fragment index and semantic search techniques are introduced to identify candidate fragments, and a tree editing distance algorithm calculates similarity scores. Evaluated on the Data Schema Construction System, the algorithm outperforms baselines-TF-IDF and BM25 for schemas matching-in precision, recall, and F1-score. The baseline for reduced workload refers to the effort required to create schemas without recommendation. Our recommendation improves schema creation efficiency by 50.5% and reduces schema proliferation by 16.5%.