Paper Info
Title
A Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs
Abstract
In this paper, we introduce a 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 the knowledge graph Freebase, where the NSWD achieves a correlation of up to 0.58 with human similarity assessments on the established Miller-Charles benchmark of 30 term-pairs. These preliminary results indicate that the proposed NSWD is a promising approach for assessing semantic dissimilarity in very large knowledge graphs.
Year
DOI
Venue
2016
10.1109/ICSC.2016.55
2016 IEEE Tenth International Conference on Semantic Computing (ICSC)
Keywords
Field
DocType
semantic distance,semantic similarity,semantic web,knowledge graphs,named entities
Semantic similarity,Data mining,Normalization (statistics),Computer science,Semantic Web,Semantic property,Knowledge engineering,Encyclopedia,Semantics,Semantic computing
Conference
ISSN
Citations 
PageRank 
2325-6516
2
0.38
References 
Authors
11
11
Name
Order
Citations
PageRank
Tom de Nies112717.70
Christian Beecks243139.14
Fréderic Godin310210.62
Wesley De Neve452554.41
Grzegorz Stepien5111.20
dorthe arndt6124.65
Laurens De Vocht717122.13
Ruben Verborgh8630105.49
Thomas Seidl93515544.45
Erik Mannens1067199.58
Rik Van de Walle112040238.28