Leveraging linked data analysis for semantic recommender systems

Abstract

Traditional (Web) link analysis focuses on statistical analysis of links in order to identify "influencial" or "authorative" Web pages like it is done in PageRank, HITS and their variants [10]. Although these techniques are still considered as the backbone of many search engines, the analysis of usage data has gained high importance during recent years [12]. With the arrival of linked data (LD), in particular Linked Open Data (LOD), new information relating to what actually connects different vertices is available. This information can be leveraged in order to develop new techniques that efficiently combine linked data analysis with personalization for identifying not only relevant, but also diverse and even missing information. © 2012 Springer-Verlag.