# Towards a Semantics-Based Recommendation System for Cultural Heritage Collections
## 2 Background and Related Work
### Graph Databases for Semantic Recommendation  
Graph databases have recently become an alternative and more efficient (compared to relational databases) way to store and manage highly relational data. They rely on a graph-based data model, called a property graph, with two main types of elements: vertices, which represent entities, connected by edges representing their relationships. Entities and relationships may also have attributes, which are used to further describe them. An example of one such graph, which represents the same information as the RDF graph shown in Figure 1, is depicted in Figure 2. Relationships are treated equally important as entities, which makes graph databases suitable for queries concerning paths and relationships, the centrality of nodes, and pattern-matching problems [26]. Such types of queries are often used by recommendation systems, which are based on representing and identifying associations among users and items [27]. Answering queries in a graph database usually involves accessing only a portion of the graph; this ensures a constant performance, which is not affected by the size of the database [27].  
The similar graph-based structures of RDF datasets and graph databases enable their interoperability, making the use of graph databases to store, manage, and analyse RDF data a possible and very efficient solution. For example, it is possible to develop a semantic recommendation system that uses RDF to model and interchange data over the Web and graph databases to implement the recommendation algorithms, taking advantage of their advanced features with respect to access speed, storage efficiency, and application-specific algorithms and tools [28,29].  
Recommendation systems powered using graph databases have been developed for various domains, such as for recommending movies [30], books [31], jobs [32], etc., while graphs have also been used as the underlying data structure for deep learning-based recommendation systems (see [33] for a relevant tutorial and related references). In the domain of cultural heritage, [PERSON] et al. [34] developed a hybrid recommendation system, the Cicero recommender, for suggesting cultural itineraries to users. The system combines two recommendation sub-systems, one based on a graph database storing information about the users' social networks obtained from Facebook, and the other based on an RDF database with a SPARQL endpoint used to retrieve related cultural heritage data from DBpedia and Europeana [34]. Another example is the system developed by [29], which integrates data from Wikidata, textual information from Wikipedia, and image-based information from Flickr and uses a graph database implemented in Neo4j to store and manage information about the cultural resources of Italy. Algorithms supported by Neo4j such as Page Rank are used to measure the popularity of Italian cities as cultural signtseeing  
Figure 2: A property graph that is semantically equivalent to the RDF graph in Figure 1.  
spots taking account of the connections between cities based on pictures taken by Flickr users [29].