The Tourism-Spain ontological model

The fragmentation of the tourism ecosystem

The Spanish tourism sector faces the challenge of operating in a fragmented digital environment where tourism information is dispersed, systems lack interoperability, and there is no capacity to offer personalised experiences to travellers. The complexity of the tourism domain — with multiple players (destinations, service providers, tourists) operating in isolation — makes it impossible to harness the potential of digital transformation to improve the sector's competitiveness and the visitor experience.

This fragmentation manifests at several critical levels: the absence of a common framework for representing and sharing tourism information, heterogeneous systems unable to exchange data effectively, an inability to personalise services according to the specific needs of each type of tourist, and difficulty in addressing the breadth of the tourism sector with its many interrelated subdomains. At the heart of all these problems lies a fundamental gap between humans and machines: digital systems cannot understand the tourism context the way people do.

A tourist looking for rural tourism experiences in northern Spain, for example, does not receive results that coherently and personally integrate rural accommodation, hiking routes, local gastronomy, cultural events and transport services. Every search returns fragmented information from disconnected sources, forcing the user to manually piece together their tourism experience. Providers, for their part, lack a common language for describing their services, which hampers the visibility and coordinated marketing of Spain's tourism offer.

For machines to be able to hold meaningful conversations with people in the tourism domain, it is essential to project onto them the structure through which humans understand the tourism world. Without this capacity for contextual understanding, digital systems are limited to interpreting keywords without grasping the user's real intent, their implicit preferences, or the complex relationships between elements of the tourism ecosystem. The sector needs an "artificial mind" capable of understanding and processing the tourism domain with the same depth and nuance as an experienced professional.

An ontology operable by machines and people

The solution does not simply catalogue tourism elements — it models the complex relationships between them in a way that allows machines to reason about the domain. An ontology projects onto machines the structure through which people understand the world, enabling them to comprehend and process it as we do. This fundamentally transforms the capability of digital systems: they move from processing words to understanding contexts, intentions and needs.

To bring these benefits to life, the knowledge structure of the tourism domain is organised into three core subdomains, within which specific vocabularies, taxonomies, classes, properties and relationships for tourism activity are deployed:

• Subdomain 1: Supply. Encompasses the providers of the full range of products and services involved in tourism activity, which may be found both within and outside the destination, including permanent and non-permanent elements.
• Subdomain 2: Tourist. Refers exclusively to the tourist as a user, placing them at the heart of tourism activity and recognising that they have different travel motivations and consumption patterns — meaning that supply must respond to those specific expectations and needs.
• Subdomain 3: Destination. Refers to the sum of the public spaces and services involved in tourism activity, generally provided by local authorities.

The interrelationship between supply, tourist and destination forms the conceptual core that makes it possible to model the complexity of the tourism system in a comprehensible and operational way. Use cases act as a long tail that allows the model to be tailored to the specific characteristics of the wide variety of tourism typologies, whilst interaction is the ingredient that enables the three subdomains of the ontological model to be combined and intertwined, covering the needs of the tourist before, during and after the trip.

Regulatory foundations, architecture and model components

The reference ontological model is grounded in Standard UNE 178503:2019 "Smart Tourist Destinations. Semantics applied to tourism" and in prior research on semantic reference models related to tourism. In the process of structuring the Spanish tourism domain, and with the aim of aligning the ontological model with SEGITTUR's Intelligent Destinations Platform (IDP), the interaction model identified in that platform is used as a reference: Supply – Tourist – Destination.

The resulting ontological model offers a well-defined global view of the tourism domain, guiding the selection of its constituent entities and effectively representing the structure and functioning of the tourism market in the current context of digital transformation.

The entities that make up the tourism ontological model are organised under a taxonomy based on purely business criteria, which facilitates the conceptualisation of the tourism domain:

• Main classes, with their subclasses: These are the central classes of the ontological model for the tourism domain, corresponding to the supply, tourist and destination subdomains. The main classes comprise the fundamental categories and the group of subclasses that share common characteristics with the category they represent. This is a hierarchy that can extend to more than one level, in which more specific entity types (subclasses) inherit the characteristics of more general entities (classes).
• Specific classes: These are particular classes within the tourism domain that apply to one or more main classes and complement the information contained in the main classes and subclasses. They operate outside the hierarchy of those entities and therefore form their own classification. They can in turn act as containers grouping related entities (specific subclasses).
• Business identification classes: These refer to the set of property groupings shared by the majority of main classes and specific classes, as they contain the attributes that allow tourism supply and destinations to identify themselves and provide relevant information for tourists. Examples include description, contact information, location, recognition and social media profile.
• Transactional classes: These comprise the set of property groupings used by the main classes corresponding to tourism product or service providers, and the specific classes corresponding to those products or services, to provide information about their booking, payment and pricing processes.
• Normalised lists: These are open or closed sets of lists containing multiple options for a given attribute. In some cases, normalised lists can accept more than one value for the same attribute when it is multi-valued.
• Thesauri: These are open lists containing multiple options for a given attribute. Unlike normalised lists, thesauri are multi-level, making it possible to reflect a hierarchy between the options they contain.

The ontological model constitutes a robust conceptual structure that comprehensively represents the key elements of tourism. As of 30 July 2023, its scope comprises 12 main classes, 81 subclasses, 136 second-level classes, 21 specific classes, 9 business identification classes, 3 transactional classes, 35 normalised lists, 3 thesauri, 21 tourism typologies, 29 interaction points and 11 coordinated SEGITTUR use cases.

This conceptual architecture allows the system to understand, for example, that a tourist interested in wine tourism in La Rioja is not simply looking for wineries to visit, but for a set of related experiences that include rural accommodation, local gastronomy, scenic routes, cultural events connected to wine, and suitable transport services. The ontological model automatically connects all of these disparate elements into a coherent, personalised value proposition.

Deploying the Tourism Ontology has a direct impact on the user experience. By taking a semantic approach, it becomes possible to offer tourists more relevant and personalised recommendations, tailored to their specific preferences and needs. Machines move from interpreting keywords to understanding the context in which they must deliver a response. This improves user satisfaction, enriches the tourist experience by providing easy access to relevant and personalised information, and increases the propensity for tourist spending.

• Personalised experiences: Relevant recommendations tailored to each tourist's specific preferences and needs, increasing satisfaction and propensity to spend.
• Sectoral interoperability: A common framework for data exchange between all players in the tourism ecosystem, eliminating information silos.
• Contextual understanding: Machines move from interpreting to genuinely understanding the tourism context, radically improving the quality of responses.
• Better decision-making: Both providers and tourists can make informed decisions at every stage of the journey.
• Accelerated innovation: A solid foundation for the development of intelligent applications and new digital business models.
• Regulatory leadership: Driving the update of public standards and industry norms.
• Data economy: Fostering an ecosystem where tourism data generates shared value.
• Sustainability and efficiency: Optimisation of resources and more sustainable, inclusive tourism development.

The resulting reference ontological model offers a well-defined global view of the tourism domain, guiding the selection of its constituent entities and effectively representing the structure and functioning of the tourism market in the current context of digital transformation. The interrelationship between supply, tourist and destination forms the conceptual core that makes it possible to model the complexity of the tourism system in a comprehensible and operational way.