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

arXiv: https://arxiv.org/abs/2604.27410 · HTML: https://mlsanity.com/paper/2604.27410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023)
- [Semantic Product Search](https://mlsanity.com/paper/1907.00937/) (2019)
- [The Amazon Nova Family of Models: Technical Report and Model Card](https://mlsanity.com/paper/2506.12103/) (2025)
- [KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models](https://mlsanity.com/paper/2310.11220/) (2023)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction](https://mlsanity.com/paper/2108.08468/) (2021)
- [Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages](https://mlsanity.com/paper/2312.16159/) (2023)
- [Unsupervised, Efficient and Semantic Expertise Retrieval](https://mlsanity.com/paper/1608.06651/) (2016)
- [Multi-Label Zero-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2402.08802/) (2024)
- [Generative Product Recommendations for Implicit Superlative Queries](https://mlsanity.com/paper/2504.18748/) (2025)
- [EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction](https://mlsanity.com/paper/2406.06839/) (2024)
- [Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition](https://mlsanity.com/paper/2511.13994/) (2025)

## Abstract (arXiv)

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.