From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking

• URL: http://arxiv.org/abs/2604.27410v1
• Date: Thu, 30 Apr 2026 04:22:20 GMT
• Title: From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
• Abstract summary: We present a two-stage approach combining Large Language Model (LLM)-driven attribute graph construction with graph-aware LLM ranking.<n>In the offline stage, we extract structured product attributes from unstructured text, and construct a reusable attribute graph with category-aware schemas.<n>In the online stage, we rank retrieved candidates by reasoning over this structured representation rather than raw text, reducing per-product token usage by 57% while improving ranking precision.
• Score: 20.966359103135762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies across categories and contexts. Traditional embedding-based approaches often struggle to capture nuanced context-specific attribute relevance. In this paper, we present a two-stage approach combining Large Language Model (LLM)-driven attribute graph construction with graph-aware LLM ranking. In the offline stage, we extract structured product attributes from unstructured text, and construct a reusable attribute graph with category-aware schemas. In the online stage, we rank retrieved candidates by reasoning over this structured representation rather than raw text, reducing per-product token usage by 57% while improving ranking precision. Experiments show that our approach outperforms multiple baselines under zero-shot scenarios, achieving a over 5% improvement in average precision without requiring training data, generalizes robustly across diverse product categories, and shows immense potential for real-world deployment.

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