用双知识图谱统一推理旅行评论中的用户意图，效果优于现有方法。
Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference
- 构建用户专属与全局酒店知识图谱，通过结构感知对齐增强语义理解。
- 在TripAdvisor数据集上，分类与意图生成任务均超越强基线模型。
- 适合需要可解释性多任务意图分析的研究者与产品团队使用。
本文提出DKG-MTI，一种用于统一多任务用户意图推断的双知识图谱框架，基于在线旅行评论。现有方法常依赖易传播错误的分层流水线，或忽略领域知识的结构关系。为此，我们设计了一种仅推理的知识增强框架，从每条评论动态构建用户特定意图知识图谱，并通过结构感知语义平滑与全局酒店知识图谱对齐。对齐后的知识与原始评论一同输入大语言模型，实现方面评分预测与反向用户意图表述生成。在TripAdvisor评论上的实验表明，DKG-MTI在分类与意图生成任务中持续优于强大的LLM与检索基线，证明了结构感知知识对齐在可扩展且可解释意图推断中的有效性。
原文摘要 · Abstract (English)
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.
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