text: The introduction of the schema.org vocabulary was a big step towards making websites machine read- and understandable. Due to schema.org's RDF-like nature storing annotations in a graph database is easy and efficient. In this paper the authors show how they gather touristic data in the Austrian region of Tirol and provide this data publicly in a knowledge graph. The definition of subsets of the vocabulary is followed by providing means to map data sources efficiently to schema.org and then store the annotated content into the graph. To showcase the consumption of the touristic data four scenarios are described which use the knowledge graph for real life applications and data analysis.
  @cite_5: The tourism industry has a significant impact on the world’s economy, contributes 10.2 of the world’s gross domestic product in 2016. It becomes a very competitive industry, where having a strong online presence is an essential aspect for business success. To achieve this goal, the proper usage of latest Web technologies, particularly schema.org annotations is crucial. In this paper, we present our effort to improve the online visibility of touristic service providers in the region of Tyrol, Austria, by creating and deploying a substantial amount of semantic annotations according to schema.org, a widely used vocabulary for structured data on the Web. We started our work from Tourismusverband (TVB) Mayrhofen-Hippach and all touristic service providers in the Mayrhofen-Hippach region and applied the same approach to other TVBs and regions, as well as other use cases. The rationale for doing this is straightforward. Having schema.org annotations enables search engines to understand the content better, and provide better results for end users, as well as enables various intelligent applications to utilize them. As a direct consequence, the region of Tyrol and its touristic service increase their online visibility and decrease the dependency on intermediaries, i.e. Online Travel Agency (OTA).
summary: The Tyrolean Tourism Knowledge Graph contains static (e.g. phone number, address) and dynamic (e.g. accommodation offers) data based on schema.org annotations collected from different sources such as Destination Management Organizations (DMO) and Geographical Information Systems (GIS). In our previous work @cite_5 , we explained how we annotated the relevant data in the region with schema.org from different sources. Our knowledge graph consolidates these annotations and enables intelligent applications like chatbots to contribute the digitalization of tourism in Tyrol. Additionally, since we store the historical data, the knowledge graph allows data analytics to provide insights from the region.