# Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
## 3 Methodology
### Tourism Knowledge Graph
#### 3.1.2 Building Tourism Knowledge Graph  
Ontology is a description of the nature and laws of things, and is an abstract model of the real world established by describing the concepts in the Knowledge Graph. There are generally two ways to build domain ontology: top-down and bottom-up. The top-down construction method first determines the data model of the Knowledge Graph, then fills in the concrete data according to the model, and finally forms the Knowledge Graph. This method is suitable for the industry Knowledge Graph. For an industry, data content and data organization modes are easier to determine [14]. Because the tourism KG involves a large amount of data, and rich information is included between scenic spots and scenic spots, the accuracy and diversity of the travel recommendation system can be improved by building the Graph from top to bottom. The Bottom-up construction method is to collect concrete data in the form of triples, and then refine the data model according to the data content. Because the public domain involves huge amounts of data and includes all aspects of knowledge, the general public domain Knowledge Graph uses this method. The effect is large and complete. In the early stage of construction, it is difficult to set the overall structure of the data, and the data frame model can be formed by summarizing and refining the features according to the content of the data. In summary, this paper adopts a top-down method, and the specific steps are as follows:  
1. Defining the entities and related concepts in the field of tourism. The attribute of tourist attractions include location, type, dynasty, ticket price, official website, average score, opening time and so on. Among them, type, score, location will affect the recommendation result. For example (Figure 1), a tourist attraction may \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attraction** & **Geographical Location** & **Type** & **Dynasty** & **Ticket** & **Official Website** & **Rating** & **Opening Time** \\ \hline Terra Cotta & Lintong District, Xi’an & Historical site & Qin & 150 yuan & [[http://bmy.com.cn/](http://bmy.com.cn/)]([http://bmy.com.cn/](http://bmy.com.cn/)) & 4.6 & 8:30–17:30 \\ Warriors & City, Shaanxi Province & Historical site & Republic & [[http://www.hqc.cn](http://www.hqc.cn)]([http://www.hqc.cn](http://www.hqc.cn)) & 4.7 & 9:00–17:00 \\ Huaqing Pool & Lintong District, Xi’an & Mountain site & Republic & [[http://www.hqc.cn](http://www.hqc.cn)]([http://www.hqc.cn](http://www.hqc.cn)) & 4.7 & 9:00–17:00 \\ Bell Tower and & Beilin District, Xi’an & Bili District, Xi’an & Historical site & Ming & 50 yuan & [[http://www.xazgl.com](http://www.xazgl.com)]([http://www.xazgl.com](http://www.xazgl.com)) & 4.5 & 8:30–20:30 \\ Drum Tower & City, Shaanxi Province & Historical site & Tang & 50 yuan & [[http://www.xadaya](http://www.xadaya)]([http://www.xadaya](http://www.xadaya)) & 4.5 & 9:00–17:00 \\ Giant Wild & Yanta District, Xi’an & Historical site & Tang & 50 yuan & [[http://nta.com](http://nta.com)]([http://nta.com](http://nta.com)) & 4.5 & 9:00–17:00 \\ Goose Pagoda & City, Shaanxi Province & Mountain & Modern & [[http://www.cuihua](http://www.cuihua)]([http://www.cuihua](http://www.cuihua)) & 4.3 & 8:00–19:00 \\ Tsui Wah Shan & City, Shaanxi Province & landscape & Times & 45 yuan & 4.3 & 8:00–19:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of data collection. belong to different types. Therefore, this paper defines tourist attractions, the type of tourist attractions, the average score of tourist attractions, and the location of tourist attractions as entities. 2. Name entity recognition. Name entity recognition originally uses a rule-based and dictionary-based method. The domain experts make effective rules to identify entities in the text, but this method is time-consuming, labor-intensive, and poorly applicable. Deep learning can automatically learn features that are effective for the target, and can achieve good results without relying on feature engineering and domain knowledge.