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

DevFeed: [Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions](<https://devfeed.tech/articles/crowdsourced-taxonomy-verification-a-feedback-driven-framework-for-refining-knowledge-graph-relationships-via-online-search-interactions-1246.md>)

Original publisher: [Read original article](<https://engineering.grab.com/crowdsourced-taxonomy-verification>)

Author: Junpeng Niu

Published: 2026-07-30T00:00:00Z

Content type: article

Language: en

Sources: [Grab Tech](<https://devfeed.tech/sources/grab-tech.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [data](<https://devfeed.tech/tags/data.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [knowledge-graph](<https://devfeed.tech/tags/knowledge-graph.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [search](<https://devfeed.tech/tags/search.md>), [validation](<https://devfeed.tech/tags/validation.md>)

## AI overview

The article presents a feedback-driven system for verifying knowledge-graph relationships through live search interactions. It tests unverified parent-child or sibling edges in search suggestions, measures user engagement, and uses greedy exploration to balance data collection with result quality.

## Source excerpt

Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, products, and merchants changes rapidly. Current methods for KG construction and maintenance face three critical challenges: Inaccuracy and hallucination from Large Language Models (LLMs): Automated models often infer relationships based on statistical text co-occurrence rather than semantic reality. For instance, an LLM might incorrectly classify "Pho" as a child of "Italian Noodle Soup" due to linguistic similarity, leading to irrelevant search results. Scalability limits of manual verification: Traditional verification relies on human annotators or domain experts. This approach is slow, expensive, and unable to keep pace with dynamic catalogs containing millions of entities. For example, daily changes in restaurant menus or grocery stock keeping units (SKUs). Error propagation in ranking: Inaccurate graph edges propagate errors downstream. If a parent-child relationship is wrong, query expansion algorithms will retrieve irrelevant items, directly degrading Click-Through Rate (CTR) and user trust. We introduce a feedback-driven verification engine that operationalizes the search interface as a validation environment. Key contributions include: User feedback-driven verification: The system treats unverified graph edges as hypotheses. Instead of accepting them as truth, it tests them against live traffic by injecting them into search suggestions and measuring user engagement. Hierarchical relationship refinement: Unlike systems that only validate entities (nodes), this framework validates structural links (edges). It confirms whether entity A is truly a parent, child, or sibling of entity B, ensuring structural integrity. Adaptive exploration: The system employs a greedy exploration policy. It intelligently balances exploitation by showing known good resul