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A distance-based approach for semantic dissimilarity in knowledge graphs

Tom De Nies, Christian Beecks, Fréderic Godin UGent, Wesley De Neve UGent, Grzegorz Stepien, Dörthe Arndt UGent, Laurens De Vocht, Ruben Verborgh UGent, Thomas Seidl, Erik Mannens UGent, et al. (2016) IEEE International Conference on Semantic Computing. p.253-256
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
Dissimilarity, Semantic Distance, Knowledge Graph, Normalized Web Distance, SIMILARITY, RETRIEVAL
in
IEEE International Conference on Semantic Computing
editor
Lisa O'Connor
issue title
2016 IEEE TENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC)
pages
253 - 256
publisher
IEEE
conference name
10th IEEE International Conference on Semantic Computing (ICSC)
conference location
Laguna Hills, CA, USA
conference start
2016-02-04
conference end
2016-02-06
Web of Science type
Proceedings Paper
Web of Science id
000382051400046
ISSN
2325-6516
ISBN
978-1-5090-0662-5
DOI
10.1109/ICSC.2016.55
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8083725
handle
http://hdl.handle.net/1854/LU-8083725
date created
2016-09-21 15:37:50
date last changed
2016-12-19 15:36:39
@inproceedings{8083725,
  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.},
  author       = {De Nies, Tom and Beecks, Christian and Godin, Fr{\'e}deric and De Neve, Wesley and Stepien, Grzegorz and Arndt, D{\"o}rthe and De Vocht, Laurens and Verborgh, Ruben and Seidl, Thomas and Mannens, Erik and Van de Walle, Rik},
  booktitle    = {IEEE International Conference on Semantic Computing},
  editor       = {O'Connor, Lisa},
  isbn         = {978-1-5090-0662-5},
  issn         = {2325-6516},
  keyword      = {Dissimilarity,Semantic Distance,Knowledge Graph,Normalized Web Distance,SIMILARITY,RETRIEVAL},
  language     = {eng},
  location     = {Laguna Hills, CA, USA},
  pages        = {253--256},
  publisher    = {IEEE},
  title        = {A distance-based approach for semantic dissimilarity in knowledge graphs},
  url          = {http://dx.doi.org/10.1109/ICSC.2016.55},
  year         = {2016},
}

Chicago
De Nies, Tom, Christian Beecks, Fréderic Godin, Wesley De Neve, Grzegorz Stepien, Dörthe Arndt, Laurens De Vocht, et al. 2016. “A Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs.” In IEEE International Conference on Semantic Computing, ed. Lisa O’Connor, 253–256. IEEE.
APA
De Nies, T., Beecks, C., Godin, F., De Neve, W., Stepien, G., Arndt, D., De Vocht, L., et al. (2016). A distance-based approach for semantic dissimilarity in knowledge graphs. In L. O’Connor (Ed.), IEEE International Conference on Semantic Computing (pp. 253–256). Presented at the 10th IEEE International Conference on Semantic Computing (ICSC), IEEE.
Vancouver
1.
De Nies T, Beecks C, Godin F, De Neve W, Stepien G, Arndt D, et al. A distance-based approach for semantic dissimilarity in knowledge graphs. In: O’Connor L, editor. IEEE International Conference on Semantic Computing. IEEE; 2016. p. 253–6.
MLA
De Nies, Tom, Christian Beecks, Fréderic Godin, et al. “A Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs.” IEEE International Conference on Semantic Computing. Ed. Lisa O’Connor. IEEE, 2016. 253–256. Print.