Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions

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Introduced a feedback-driven verification engine for knowledge graphs. This engine operationalizes the search interface as a validation environment by injecting unverified candidate edges into search suggestions and measuring user engagement. Key components include user feedback-driven verification, hierarchical relationship refinement, and adaptive exploration. The system categorizes edges into 'verified' and 'candidate' states, uses a search and injection module with an exploration vs. exploitation strategy, tracks user micro-interactions with contextual anchoring and signal classification, and employs a verification and refinement engine for graph topology updates based on relevance scoring and weighted interaction logic.