Hypothesis-Driven Semantic Retrieval for Discovering Unseen Knowledge Structures

This work proposes a hypothesis-driven retrieval paradigm for discovering unseen knowledge schemata from text corpora without supervision. The framework generates hypothetical semantic signals derived from linguistic regularities and validates them through evidence retrieval across the corpus. A signal verification module filters inconsistent hypotheses, while a semantic clustering component consolidates validated structures. The system delivers notable improvements on OpenSchema-ZS, CorpusGraph-ZS, and Narrative-ZS datasets, achieving +19.5%, +21.3%, and +17.8% gains in structural retrieval accuracy. It also reduces schema hallucination errors by 28.4%. Human judges rate the extracted structures 24.9% higher on interpretability metrics.