Recommendations and Object Discovery in Graph Databases Using Path Semantic Analysis

This paper presents a novel approach to recommendation systems based on graph databases (e.g. LinkedData). Graph databases contain large amounts of heterogeneous and interlinked data from many sources, hence different algorithms to analyze these data are necessary. Moreover, it must be said that these properties of collected data make it impossible to store them in a relational data model. As for now, there are few methods of intelligent data exploration from graph databases.