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A distance-based approach for semantic dissimilarity in knowledge graphs

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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.
Keywords
Dissimilarity, Semantic Distance, Knowledge Graph, Normalized Web Distance, SIMILARITY, RETRIEVAL

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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.
@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},
}

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