Schema-Guided Hierarchical Information Extraction Using Generative AI
A new framework for extracting structured information from unstructured text using generative AI has been introduced in a paper on arXiv (2608.06167). The schema-based approach encodes domain knowledge to enable consistent extraction of hierarchical, nested information with variable-cardinality attributes, all in a single zero-shot call. Evaluation employs a path-based semantic matching algorithm and a rubric for semantic comparison against a gold standard. This work, relevant to digital art platforms and archives, promises improved accuracy in automated metadata extraction.
Key facts
- Framework uses schema-based approach for information extraction.
- Extraction performed in single zero-shot call to generative AI.
- Supports hierarchical, nested information with variable cardinality.
- Evaluation uses path-based semantic matching algorithm.
- Semantic comparison done via generative AI and rubric.
- Paper available on arXiv with ID 2608.06167.
- Announcement type: new.
Entities
Institutions
- arXiv