# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 6 Conclusions  
In this study, we developed a theory-guided representation learning approach that integrates multiple views--including chatbot users, system developers, and medical experts--to generate informative query representation for unknown intent detection. Our experiments on a real-world healthcare query dataset from the Tianchi laboratory demonstrated the effectiveness and robustness of the proposed method. While the performance  
Figure 4: Ablation study on multi-view combinations across evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**View Assembly** & **Accuracy** & **F1** & **F1-Known** & **F1-Unknown** \\ \hline UV & 0.5058 & 0.4224 & 0.3424 & 0.5823 \\ \hline UV + SDV & 0.5165 & **0.4482** & **0.3777** & 0.5893 \\ \hline UV + MEV & 0.5181 & 0.4230 & 0.3406 & 0.5878 \\ \hline UV + SDV + MEV & **0.5213** & 0.4477 & 0.3742 & **0.5948** \\ \hline \end{tabular}
\end{table}
Table 8: Ablation study under the unknown intent ratio of 75%.  
of all methods declined as the unknown intent ratio increased, our approach consistently outperformed benchmark models despite some degradation. By integrating diverse perspectives, we solve the problem from the perspective of inconsistent public expertise reserves. Future work could explore dynamic strategies for adjusting the view combination to further facilitate unknown intent detection.  
While our method demonstrates strong performance in Chinese healthcare scenarios, its extension to other languages or professional domains requires careful linguistic adaptation and localization of expert knowledge. The model's effectiveness depends on maintaining linguistic consistency across user queries, intent labels, and knowledge base to ensure proper attention computation and entity matching. This framework can be adapted to other specialized domains where structured knowledge bases exist, provided that domain-specific query corpora are collected and corresponding intent classification schemes are designed. For non-Chinese implementations, language-specific pre-trained models and localized knowledge graphs would need to be integrated while preserving the multi-view learning architecture. The core boundary learning mechanism remains fundamentally transferable, providing both conceptual and methodological insights for unknown intent detection and domain-specific chatbot deployment.  
Conceptualization, Y.Z.; methodology, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; data curation, Y.Z.; writing\(-\)original draft preparation, Y.Z.; writing\(-\)review and editing, Y.Z. and R.Y.K.L.; visualization, Y.Z.; supervision, R.Y.K.L.; funding acquisition, R.Y.K.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, Grant No. CityU 11507323.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
This research work was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region (Project: CityU 11507323).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: