LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources
Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles. Variations in layout, terminology, and data quality impede rapid triage, large-scale analysis, and search-planning workflows. This paper introduces the Guardian Parser Pack, an AI-driven parsing and normalization pipeline that transforms multi-source investigative documents into a unified, schema-compliant representation suitable for
Record details
Published: 8 April 2026
Source: arXiv
Category: Research
Topics: Children & education · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (8 April 2026), “LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources,” evidence record 6218, https://ethics.ai/record/6218 (originally published by arXiv).
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