Semi-automatic construction of heterogeneous data schema based on structure and context-aware recommendation
Year: 2025
Type: data-paper
Abstract: 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%.
Source: Scientific Data
Authors: Nan Yin, Junheng Liang, Xi Guo, Xue Jiang, Jie He, Xiaotong Zhang
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W4407056751