# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 2 Literature Review and Design Theories
### Design Theories and Multi-View Representation Learning  
When facing uncertainty, humans will seek \"fair\" information to discover new knowledge and utilize the information for strategic purposes, according to uncertainty management theory [43]. In alignment with this theory, we aim to empower healthcare chatbots with a similar ability to access external information and mitigate the impact of uncertainty on decision-making. In a typical healthcare e-service setting, three different roles are involved as follows: chatbot users (who initiate queries), system developers (who design Q&A workflows and define intent categories), and medical experts (who provide medical expertise). From the developers' view, they pre-define a series of intent categories to help chatbots understand user queries. As for the experts' view, they provide medical information to help produce appropriate responses. This medical information is often stored and maintained as the data format of a knowledge base in an electronic medical system. To sum up, the developer view is encoded in intent labels, while the medical expert view is derived offline from the knowledge graph, avoiding reliance on real-time clinician input.  
Existing methods only capture semantic information from a single user view, which is, as we argue, insufficient in a professional and demanding scenario. Following the abovementioned theory, we propose that a chatbot can be equipped with knowledge from two \"fair\" views of system developers and medical experts when processing user queries with uncertain intent. To support this approach, we develop a multi-view processing schema to guide our representation design based on the social information processing theory [44; 45; 46]. This theory explains how individuals process social information through a series of learning processes, such as selective attention [47; 48], interpretation [49; 50; 48], and integration [47; 48].  
Figure 1 illustrates our conceptual design of multi-view representation learning, which aims to transform a user query into an informative representation that facilitates effective chatbot decision-making. In healthcare contexts, distinct perspectives--particularly those of system developers and medical experts--play a critical role. We notice that the intent category design and knowledge base can reflect how user queries are viewed from the perspectives of system developers and medical experts. System developers can enhance the chatbot response performance by pre-classifying a user query into an intent class to indicate the response-producing strategy. These intent label texts can be regarded as context information that helps emphasize the critical contents related to scene intents, so key information about known intents can be highlighted from the user query. This can better facilitate the model in unknown intent detection. We argue that the intent label texts play a \"selective attention\" role in human language understanding.  
Furthermore, medical experts often maintain and structure their knowledge using knowledge bases designed to support information retrieval in electronic medical systems [51; 52]. The knowledge base represents a network of real-world entities, such as objects, events, situations, or concepts, and illustrates their relationships. As a common technique in healthcare applications, knowledge bases promote the transfer and sharing of medical knowledge while improving service efficiency [53]. Thus, user queries can be interpreted with matched knowledge to form a more professional view, aligning user utterances with medical expertise to improve chatbots' response-producing. This represents an \"interpretation\" role for the chatbot's human language understanding. With the \"selective attention\" role of the system designer view and the \"interpretation\" role of the medical expert view, external knowledge is integrated into the user query, supporting a more comprehensive and context-aware \"integration\" of user queries.