The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.

This paper presents an intelligent user interface model dedicated to the exploration of complex databases. This model is implemented on a 3D metaphor: a virtual museum. In this metaphor, the database elements are embodied as museum objects. The objects are grouped in rooms according to their semantic properties and relationships and the rooms organization forms the museum. Rooms? organization is not predefined but defined incrementally by taking into account not only the relationships between objects, but also the user's centers of interest. The latter are evaluated in real-time through user interactions within the virtual museum. This interface allows for a personal reading and favors the discovery of unsuspected links between data. In this paper, we present our model's formalization as well as its application to the context of cultural heritage.

This paper presents a first step in the realization of an interactive user interface that organizes itself according to the user exploration of a database of cultural heritage objects. The first part makes a brief related works and lays the basis of this kind of system according to the enactive paradigm. The second part explains the mechanisms underlying the selforganization of the interface: keywords and cultural heritage objects are 3D graphical entities endowed with autonomous behaviors. They share a common virtual environment. Keywords behaviors are based on boïds flocking simulation while cultural heritage objects appears in an virtual museum which evolves and grows progressively. The result that is presented, is an incremental construction of an interactive and realtime 3D metaphor of virtual museum which is then user-specific.

The estimation of points of interest (POI) categories is essential in several contexts, such as land use estimation, POI and itinerary recommendation in the tourism sector, and so on. Most of these approaches are based on well-known POIs and use information such as people’s mobility or check-in data. This information is not or rarely available for lesser-known POIs. However, these lesser-known POIs cannot be ignored because of this lack of information, as they may be important to local people in terms of their culture and history and worth discovering by tourists or local authorities. To address this challenge, we propose an approach based on the techniques of coupling the syntactic and semantic analysis of data via a knowledge graph using Few-shot learning (FSL) and Light Graph Convolution Network (LightGCN). The FSL technique allows us to work with very little data, which not only works with lesser-known POIs but also reduces the complexity in terms of tasks and execution time. The results show that our approach outperforms the baseline approaches and that considering the semantic aspect of the data via Linked Open Data (LOD) significantly improves the results of the approach based on the syntactic analysis alone.