Application of Knowledge Graphs in the Internet Tourism Industry

With the rapid growth of the Internet and big data, the volume of data has exploded, and knowledge graphs, with their strong semantic processing and open organization capabilities, lay the foundation for knowledge organization and intelligent applications in the big‑data era.

The tourism industry, encompassing transportation, sightseeing, accommodation, dining, shopping, and entertainment, generates massive amounts of data across many scenarios. The breadth and depth of tourism data, such as a simple group‑tour product linking flights, hotels, attractions, and restaurants, make constructing a tourism‑domain knowledge graph a valuable challenge.

1. Characteristics of Tourism‑Domain Knowledge Graphs

Traditional relational databases require many tables to store complex, interrelated tourism data, making integration and multi‑domain reasoning difficult. By storing data in a graph database, entities such as airlines, hotels, attractions, and restaurants become nodes, relationships become edges, and attributes become node properties, enabling intuitive path traversal and inference.

For example, to find the country of the Grand Palace, one can follow the path "Grand Palace" → "located in city" → "Bangkok" → "located in country" → "Thailand"; a similar approach can retrieve nearby hotel prices, which would otherwise need complex joins in a relational database.

The graph also supports semantic abstraction: entities can be classified into classes (e.g., "Ancient Architecture"), inheriting attributes from parent classes, reducing the need to define common properties for each subclass.

2. Construction of the Tourism Knowledge Graph

Construction combines internal enterprise data with external sources. Internal data provides the core product information (e.g., hotel room types, addresses), while external data such as Wikipedia supplies broad common‑sense facts (e.g., the area or height of the Grand Palace). The integration yields a deep, industry‑specific graph complemented by a wide‑coverage generic graph.

External textual data is processed using natural‑language‑processing techniques: named‑entity‑recognition (NER) extracts entities, and syntactic analysis or distant supervision extracts relationships. For instance, from a paragraph mentioning "Liu's Manor" located in "Xingyi", the triple "Liu's Manor" → "located in" → "Xingyi" is extracted.

Storage options include RDF triple stores (standardized, with built‑in reasoning engines) or graph databases (nodes and edges with attributes, offering higher traversal performance for large‑scale enterprise data).

After the graph is built, it can be enriched through link prediction methods such as TransE, TransH, or TransR to fill missing relationships.

3. Applications of the Tourism Knowledge Graph

• Travel Q&A Bots : By mapping question entities and relations to the graph, a Knowledge‑Based Question Answering (KBQA) system can answer "what", "when", and "where" queries directly from graph data.

• Travel Content Recommendation : When a user’s interests (e.g., islands, five‑star hotels) are linked to graph entities, the graph supplies additional contextual information that enhances recommendation algorithms.

• Travel Content Search : Complex queries like "hotels near the Grand Palace" or "price of hotels near the Grand Palace" are resolved by traversing the graph (e.g., "Grand Palace" → "nearby hotel" → "price"), leveraging the graph’s efficient traversal capabilities.

4. Conclusion

Knowledge graphs have numerous other potential uses in tourism that are not exhaustively listed here. Although the technology is still evolving, its combination with the expanding tourism market promises significant future advancements and broader applications.