Abstract. In this paper we present a methodology to provide visitors, in smart regions, additional cultural heritage attractions based on prior museum visits using user models and Linked Open Data. Visitor preferences and behavior are tracked via a museum mobile guide and used to create a visitor model. Semantic models and Linked Open Data support the representation of regional assets as Cultural Objects. The visitor model preferences are exploited using a graph similarity approach in order to identify personalized opportunities for visitors by filtering relevant Cultural Objects. Keywords: Personalization, User Models, Linked Open Data, Smart Regions Introduction In this short paper we show a blueprint how semantic models and Linked Open Data (LOD) support the representation of regional assets in order to identify categories of opportunities for visitors based on different personal characteristics determined by previous visits. Having a broad infobase from which to cull possibilities is an arduous task that can benefit from automation. Due to the overwhelming number of possibilities, it is important to personalize the Cultural Heritage (CH) experience. When considering what is requires from a smart, personalized system, it becomes clear that the reasoning process of the system has to focus on identifying opportunities for intervention. When and how to intervene and what information to deliver/service to offer. Having a user model, a context model, and a model of the cultural objects are essential for successful support. These can lead to the interaction of museums and places of cultural heritage to create mega-tourist experience (similar to Verbke and Rekom [6] concept of the "museumpark") which can have a positive market effect for the region. We describe our methodology: First we use exhibits in a museum (we use Castle Buonconsiglio in the Trentino Region as examples throughout this paper) and tag them using semantic concepts. Then a mobile museum guide is used to track visitors
