## 3 Proposed System Technology, Architecture, and Operation
### Building UAI-Based LOD Access Technology  
In the earlier stage of this study, the solution through the Dropbox cloud space, which was researched and developed as an open data platform, and did not provide APIs for accessing the corresponding database, but only provided hyperlinks for downloading the open database. The solution to this issue is UAI technology, and the related steps are described in [5; 7]. The establishment of UAI access to the open database by the subsequent system only requires the conversion of the location of the user's local GPS to the corresponding address; it then compares the appropriate open database for the keywords of the user's current event, and then converts the corresponding map display according to the data intercepted in the \"address\" field to reach the research goal of the aforementioned value-added location-based mobile information service. Furthermore, the back-end system must first build the ontology of the corresponding event (as shown in Figure 1c) and link it to the appropriate open databases. The APIs corresponding to the map are accessed on the basis of the open database query, and the query results are then developed to complete the establishment of the whole UAI-based LOD  
Figure 1: Tourism ontology index structure and data relationship: (**a**) a two-stage index structure; (**b**) relationship between ontology index structure and real data; and (**c**) modular connection and use of context index in tourism event ontology. databases for achieving the goal of fast processing on the basis of the concept of exchanging space for time. Regarding the establishment of the UAI-based LOD cloud database, first, the back-end cloud database uses the Parse cloud database to process the JSON (JavaScript object notation) format, and then uses a Google Cloud Spreadsheet to process the CSV (comma-separated values) format. By referring to the aforementioned UAI technology, it processes files or other formats via Dropbox to collate the relevant government open data, and access system-related images and data. In general, the government open database provides two kinds of data link concatenation methods; one is that the API links provided by the government database are directly used, and the other is to download the data for use by oneself. The reason why this study did not choose the former method, which seems more direct and convenient, is that the amount of data used by the system is quite large and its content is more complicated. If the open database is directly connected in series, it will only increase the burden of interpretation and management of the system. Therefore, this system adopted the method of periodically uploading open data to the Dropbox cloud database, which facilitates the management of data categories. However, as Dropbox has not yet provided the relevant API for JavaScript, this system and Dropbox cannot be concatenated directly; therefore, the above UAI technology was used to solve this problem. This study established a related travel event ontology architecture (as shown in Figure 1c) with a back-end LOD cloud database, allowing the breadth and depth of the system to be changed according to the needs of the system. In other words, the information breadth can be adjusted by increasing or decreasing events, while information depth can be determined by matching it with the provision of back-end data and functions. The correspondence between some events and back-end data is shown in Figure 2. This not only makes it easy for the system to achieve the research and development goal of flexibly tuning the depth and breadth of recommended information, but also enables the event ontology, as established by the system, to provide block information connection according to different event situations, thus improving the system's access efficiency and response time.