*3.2. Outdoor Test*

We chose a starting point about 300 m away from the Taoyuan A17 MRT station and asked an average blindfolded person and a visually impaired person to conduct the test. The tester walked with the navigation device, communicated with the device, and followed the instructions moving forward. The travel route and dialogue content of the entire use process are shown in Table 4.

First, the tester stated he wanted to take a trip, and after communicating with the navigation system, he decided to go to A17 MRT. Still, shortly after departure, the tester said he wanted to go to the store to buy a drink. Thus, the navigation system took him to the nearest convenience store. Soon, the tester would have liked to leave the convenience store and go to the MRT again. He informed the navigator to take out the previous location and take him there.


**Table 4.** The walking route and conversation of the entire navigation.

The experiences of the two testers are described as follows.

The test of ordinary people with blindfolded eyes is very successful, even if their eyes are completely covered. Ordinary people do well in communicating with machines and have full confidence in walking. He can reach his destination with little help. It may be that they are already familiar with the system using vision.

Visually impaired people will be extra careful when using equipment in walking. Their step distance will be smaller than that of ordinary people. In conversation with the machine, they will often encounter situations where they cannot talk. The main reason is that they cannot understand the speaking of navigation and must do more training to use the system smoothly. The whole process requires more assistance to reach the destination.

#### **4. Discussion**

Our dialogue system can work well in outdoor navigation for visually impaired people. The knowledge graph provides the main contribution. The required navigation information can be correctly understood based on the context and assisted in language generation. This dialogue system provides useful help for visually impaired people walking outdoors.

In the real-world test, we also encountered traditional problems. In a noisy outdoor environment, ASR is very susceptible to environmental noise and cannot accurately obtain the speaking content by the tester, resulting in syntax analysis errors. Besides using noise reduction technology to improve the performance of ASR, we should also enhance the exceptions handling or develop a text correction system based on the knowledge graph.

Our macro-navigation uses many Google cloud services, including ASR, TTS, NLU, and DPL (Google Maps API), etc. This causes our system to rely heavily on the internet. Once the service is suspended or there is no access to the internet, the system will not work. In the follow-up jobs, we must integrate offline solutions so that the use of navigation can be freer from environmental constraints.

The knowledge graph should provide more professional content for navigation applications including more relationship attributes, such as opening hours, allowable ages, or occasional restrictions. When the knowledge graph contains more information, the navigation location suggestions will be more helpful.

#### **5. Conclusions**

Understanding the user and responding in accordance with the given context is the main goal of the dialogue system. We have designed a method for using the knowledge graph as a knowledge base for reasoning dialogue to obtain the user's destination. The same method can be extended to confirm destination changes, indoor navigation, or route planning. Our system can become a good VUI to communicate with micro-navigation, wearable devices, or any smart device.

A traditional VUI uses commands to accomplish user's requirements. It converts speech into instructions. Users must make all decisions by themselves by speaking. We hope that the services of the system can be more user-friendly, so that it can guide users to gradually discover their needs. We believe this approach is closer to human thinking.

We have designed a dialogue system that integrates knowledge graphs and uses reasoning algorithm to guide users' destinations. We proposed a concrete and feasible macro-navigation architecture, and verified it in the real-world. After the experiment, we learned the importance of handling misunderstandings and the defects of over-reliance on cloud service. In addition, we also found that by improving the professionalism of the knowledge graph content, it will be very helpful for reasoning and achieve more accurate destination.

The main contribution of this paper is the design of a dialogue system architecture based on the knowledge graph which can complete the function of the dialogue system in DST. Additionally, another contribution is the use of the PRA algorithm to implement reasoning for navigation destinations.

Human natural language is widely used and all-encompassing. The general dialogue system is still difficult to meet the needs of casual chat, but the dialogue in a specific domain may be better. The semantic scope of the navigation system is very limited and concentrated, which is very suitable to becoming the best practice for dialogue in a specific domain.

In the future, we want to use the dialogue system with domain-related knowledge graphs in other fields such as medicine, law, or insurance. An intelligence dialogue is good for both the visually impaired people and any ordinary person.

**Author Contributions:** Conceptualization, C.-H.C. and M.-F.S.; methodology, M.-F.S.; software, M.- F.S.; validation, C.-H.C., M.-F.S. and S.-H.C.; formal analysis, C.-H.C.; investigation, S.-H.C.; resources, S.-H.C.; data curation, S.-H.C.; writing—original draft preparation, M.-F.S.; writing—review and editing, C.-H.C.; visualization, M.-F.S.; supervision, C.-H.C.; project administration, C.-H.C.; funding acquisition, C.-H.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is supported by MOST Taiwan (MOST-109-2221-E-008-055 and MOST-110- 2634-F-008-005).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The financial support by MOST Taiwan and PAIR Labs contributions for this work are gratefully acknowledged.

**Conflicts of Interest:** The authors declare no conflict of interest.
