Construction and Application of a Tourism Knowledge Graph

The article begins by defining a knowledge graph as a graph‑based representation of knowledge introduced by Google in 2012, where nodes represent entities (e.g., hotels, cities, rooms) and edges represent relationships (e.g., "hotel located in Beijing").

It then motivates the need for a tourism knowledge graph by highlighting the limitations of traditional recommendation systems that rely on numerous isolated databases (hotel, flight, visa, attractions) and complex feature engineering, which can be replaced by a unified semantic graph.

It describes a generic knowledge‑graph template that can be specialized for domains such as tourism, finance, medicine, etc., and explains that a domain‑specific graph is built by filling this template with relevant entities and relations.

The tourism knowledge graph example centers on a travel product and expands to related entities like hotels, flights, destinations, restaurants, visas, and attractions. Entities are defined with attributes (e.g., hotel star rating, coordinates, price) and relationships such as "hotel distance to attraction" are modeled as edges.

The architecture consists of an upper layer (QA, recommendation, search, knowledge mining), a schema management layer (entity types, attributes, ontology), and a data layer that can import existing relational data or extract knowledge from unstructured sources. Storage combines RDF/OWL triples for schema reasoning and graph databases for high‑performance queries.

Construction steps include defining the schema, extracting entities and relations from text (using rule‑based methods, HMM, CRF, CNN+CRF, RNN+CRF), supplementing with external knowledge bases, migrating data from SQL databases, and performing entity linking and deduplication to ensure uniqueness.

Applications discussed include QA systems (NLU, DM, answer retrieval from the graph), path‑based recommendation (meta‑path, meta‑graph) and embedding‑based recommendation, as well as semantic search that leverages entity extraction for more accurate results.

Finally, the article looks ahead to integrating knowledge‑graph semantics with deep‑learning models, converting discrete triples into continuous vectors, using the graph as a constraint or prior in neural networks, and pursuing multi‑domain fusion, automatic reasoning, event graphs, and real‑time updates for open‑domain dialogue and tourism planning.