Page - 51 - in Digital Entrepreneurship - Impact on Business and Society

Artificial Intelligence andLinkedOpenDataCreate SmartDestinations Machine-readable data in the form of linked open data (LOD) and artificial intel- ligence (AI) as a generic term for different automated computing methods and machine learning (ML) are directly related. But what do these terms have in commonandwhat do theyhave to dowith tourism?With the help ofML, patterns can be recognised in data to derive predictions (Horster andKärle 2019a). Data is processed based on computingmodels or algorithms. Themore data is processed, the better the algorithm becomes. The algorithm develops independently after several rounds, therefore one speaks of AI. A machine is particularly good at interpreting data that is structured in a uniform language or ontology, e.g. schema. org. The structuring and the provision of a large amount of data, such as POI, events, hotels and personal preferences of the guests, and their connection through AIalgorithms, enable interestingbasic applications for tourism(Samochowiecet al. 2019). In the future, a smart assistant will be able to use the data to answer individual inquiries from guests or to give tips andwill be available at any timewhen trav- elling.Automated access at events, cashless payment in event parks, navigation on ski slopes or real-time information on the smart watch at the holiday location are also conceivable (Horster and Foltin 2020). To enable this scenario, however, destinationmanagement organisations have to becomedigital platforms to connect the offers of the destination with the guest and his or her preferred devices like smartphones, laptop computers, tablets or smart watches. KnowledgeGraphs depict TourismReality People andmachines can intuitively capturedata that is represented inaknowledge graph or a graph database as a network structure (Paulheim 2016). A knowledge graph describes real entities such as a hotel and a review about it, as well as the relationships of these entities to each other (Horster and Kärle 2019b). The knowledge graph is semantic because the data is givenmeaning. Thismeans that a smart assistant such as Alexa becomes better at understanding what specific information ismeantwhen a traveller asks for it. In thisway, a digital imageof the touristic reality is created piece by piece. The knowledge graph can be filled infi- nitely with data and their relevant context and is able to gradually recognise rela- tionships between these data automatically. This is a great advantageover previous relationaldatabases, theentriesofwhichcan laterbechangedor supplementedonly with great difficulty and where the data has little relationship to one another (Horster and Kärle 2019b). With the help of knowledge graphs, data can be transformed into information, which can then be transformed into knowledge and lead to actions by the guest (Stichbury 2017). Initially, only individual data is available, for example for a hotel or bike path. This data can be aggregated and assigned toaddresses,openinghoursor special offers, and thenall theseentities can be related to each other. Data relationships between a hotel and a bike path then help a guest to decide in real timewhere to stay at night, or if there is something special near his or her location. Knowledge graphs thus have a real-time influence on the specific guest behaviour during the customer journey. Digital Entrepreneurship andAgileMethods… 53