How knowledge graphs can help travel brands

How knowledge graphs can help travel brands

October 22, 2018

The best way travel brands, destinations, online travel agencies, and resorts can improve the traveler experience is by building a knowledge graph. It is the optimal method to market travel offers and prepare for the “AI-first world” of voice search and personal assistants. When building a knowledge graph for a travel brand, the focus should be on the type of information that a traveler would need before reaching a destination. That is why it is important to model data for the planning and booking moments according to Andrea Volpini, CEO and co-founder of WordLift.

Typically, planning for a trip starts when a digitally savvy traveler decides on a location and then tries to find out the right time to travel and stay. Subsequently, the traveler moves onto the booking phase. The booking phase is the one when the traveler, after deciding on the destination and time, looks for reserving the perfect hotel room. Therefore, to model hotel-related information in web content using the schema.org vocabulary, three-core type of nodes (entity types) are required.

They are as follows:

  • a lodging business (e.g. a hotel),
  • an accommodation (e.g. hotel rooms), and
  • an offer to let a hotel room to be used for a particular amount of money and for a given purpose (e.g. occupancy).

Moreover, in this model, relationships (edges in the graph) between these entities are designed to make several potential conversations between a lodging business and a prospective client possible. Furthermore, encoding these relationships using an open vocabulary will enable search engines and / or virtual assistants traverse these connections in multiple ways.

Click here to continue reading the other elements that are important for building knowledge graphs for travel brands.

Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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