Employing Link Differentiation in Linked Data Semantic Distance

The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure of LOD is utilized to measure inter-resource relatedness via their semantic distance in the graph. The intuition behind this approach is that the more connected resources are to each other, the more related they are. The drawback of this approach is that it treats all inter-resource connections identically rather than prioritizing links that may be more important in semantic relatedness calculations. In this paper, we show that different properties of inter-resource links hold different values for relatedness calculations between resources, and we exploit this observation to introduce improved resource semantic relatedness measures, Weighted Linked Data Semantic Distance (WLDSD) and Weighted Resource Similarity (WResim), which are more accurate than the current state of the art approaches. Exploiting these proposed weighted approaches, we also present two different ways to calculate links weights: Resource-Specific Link Awareness Weights (RSLAW) and Information Theoretic Weights (ITW). To validate the effectiveness of our approaches, we conducted an experiment to identify the relatedness between musical artists in DBpedia, and it demonstrated that approaches that prioritize link properties resulted in more accurate recommendation results.