Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference

2026-08-06

AI Summary

This work addresses the limitations of existing methods for inferring user intent from online travel reviews, which are often susceptible to cascading errors or neglect the structured relationships inherent in domain knowledge. To overcome these issues, the authors propose DKG-MTI, a novel framework that dynamically constructs a user-specific intent knowledge graph during inference and aligns it semantically with a global hotel knowledge graph through structure-aware mechanisms. Integrating fine-tuned large language models, the framework enables joint multi-task reasoning for aspect-level rating prediction and reverse intent generation. By incorporating a knowledge-enhanced mechanism, the approach supports interpretable and scalable intent inference. Experimental results on the TripAdvisor dataset demonstrate that DKG-MTI significantly outperforms strong baseline models in both classification and intent generation tasks.

Abstract

This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews. Existing approaches often rely on hierarchical pipelines that suffer from error propagation or retrieval methods that ignore structural relationships in domain knowledge. To address these limitations, we introduce an inference-only knowledge augmentation framework that dynamically constructs a User-Specific Intent Knowledge Graph from each review and aligns it with a Global Hotel Knowledge Graph through structure-aware semantic smoothing. The aligned knowledge is combined with the original review and processed by a large language model to simultaneously predict aspect ratings and generate reverse user intent statements. Experiments on TripAdvisor reviews show that DKG-MTI consistently outperforms strong LLM and retrieval-based baselines in both classification and intent generation tasks, demonstrating the effectiveness of structure-aware knowledge alignment for scalable and explainable intent inference.

Problem

• user intent inference
• multi-task learning
• knowledge graph
• online travel reviews
• structure-aware alignment

Innovation

• dual knowledge graph
• structure-aware alignment
• multi-task intent inference
• knowledge augmentation
• large language model