PLDSD: Propagated Linked Data Semantic Distance

Linked Open Data (LOD) has been exploited by recommender systems in different ways. One way measures the semantic distance between LOD resources so that nearby resources are considered related and can be recommended, the LDSD method exemplifies this approach. One drawback of the LDSD approach is that it can only compute the semantic distance between two resources that are directly linked or indirectly linked through an intermediate resource. Resources that are located more than two links away are automatically considered unrelated. In this paper, we introduce an approach, called Propagated Linked Data Semantic Distance (PLDSD), that expands the coverage of current semantic distance approaches. We employ an all-pair shortest path algorithm, the well-known Floyd-Warshall algorithm, to expand semantic distance calculations beyond resources that are just one or two links away. To validate the effectiveness of our approach, we conducted an experiment to identify the relatedness between musical artists in DBpedia, and it demonstrated that, by increasing the reach of semantic distance calculations, PLDSD increases the accuracy of the LOD-based recommendations over our baselines.