A Dynamic Self-Evolving Extraction System

• URL: http://arxiv.org/abs/2603.06915v1
• Date: Fri, 06 Mar 2026 22:26:35 GMT
• Title: A Dynamic Self-Evolving Extraction System
• Abstract summary: DySECT is a Dynamic Self-Evolving Extraction and Curation Toolkit.<n>It incrementally populates a self-expanding knowledge base with triples extracted by the LLM.<n>The KB further enriches itself through the integration of probabilistic knowledge and graph-based reasoning.
• Score: 9.760407087070632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-quality extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers. In many domains--such as medical, legal, and HR--the extraction model must also adapt to shifting terminology and benefit from explicit reasoning over structured knowledge. We propose DySECT, a Dynamic Self-Evolving Extraction and Curation Toolkit, which continually improves as it is used. The system incrementally populates a versatile, self-expanding knowledge base (KB) with triples extracted by the LLM. The KB further enriches itself through the integration of probabilistic knowledge and graph-based reasoning, gradually accumulating domain concepts and relationships. The enriched KB then feeds back into the LLM extractor via prompt tuning, sampling of relevant few-shot examples, or fine-tuning using KB-derived synthetic data. As a result, the system forms a symbiotic closed-loop cycle in which extraction continuously improves knowledge, and knowledge continuously improves extraction.

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