Building an Ecosystem for the Tyrolean Tourism Knowledge Graph
This work addresses the need for accessible, structured touristic data for applications in the Tyrolean tourism domain, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of making touristic data machine-readable by gathering data from the Austrian region of Tirol and providing it publicly in a knowledge graph, using schema.org vocabulary subsets and mapping techniques to store annotated content efficiently.
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.