Ruben Verborgh’s data

Matches in Ruben’s data for { ?s ?p "In this paper, we propose and investigate a novel distance-based approach for measuring the semantic dissimilarity between two concepts in a knowledge graph. The proposed Normalized Semantic Web Distance (NSWD) extends the idea of the Normalized Web Distance, which is utilized to determine the dissimilarity between two textural terms, and utilizes additional semantic properties of nodes in a knowledge graph. We evaluate our proposal on two different knowledge graphs: Freebase and DBpedia. While the NSWD achieves a correlation of up to 0.58 with human similarity assessments on the established Miller-Charles benchmark of 30 term-pairs on the Freebase knowledge graph, it reaches an even higher correlation of 0.69 in the DBpedia knowledge graph. We thus conclude that the proposed NSWD is an efficient and effective distance-based approach for assessing semantic dissimilarity in very large knowledge graphs."@en }

• ICSC.2016.55 abstract "In this paper, we propose and investigate a novel distance-based approach for measuring the semantic dissimilarity between two concepts in a knowledge graph. The proposed Normalized Semantic Web Distance (NSWD) extends the idea of the Normalized Web Distance, which is utilized to determine the dissimilarity between two textural terms, and utilizes additional semantic properties of nodes in a knowledge graph. We evaluate our proposal on two different knowledge graphs: Freebase and DBpedia. While the NSWD achieves a correlation of up to 0.58 with human similarity assessments on the established Miller-Charles benchmark of 30 term-pairs on the Freebase knowledge graph, it reaches an even higher correlation of 0.69 in the DBpedia knowledge graph. We thus conclude that the proposed NSWD is an efficient and effective distance-based approach for assessing semantic dissimilarity in very large knowledge graphs.".