arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Artificial Intelligence

arXiv:2608.06752 (cs)
[Submitted on 7 Aug 2026]

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

Authors:Tzu-Cheng Peng (1), Chien Chin Chen (1), Chih-Hao Ku (2), Yung-Chun Chang (3) ((1) National Taiwan University, (2) University of North Texas, (3) Taipei Medical University)
View a PDF of the paper titled Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference, by Tzu-Cheng Peng (1) and 5 other authors
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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.
Comments: Published in the PACIS 2026 Proceedings as a Completed Research Paper. AIS eLibrary: this https URL 17 pages, 5 figures
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2608.06752 [cs.AI]
  (or arXiv:2608.06752v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2608.06752
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of the Pacific Asia Conference on Information Systems (PACIS 2026), Paper 12, 2026

Submission history

From: Chih Hao Ku [view email]
[v1] Fri, 7 Aug 2026 03:18:13 UTC (1,205 KB)
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