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Ruben Verborgh

Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs

Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Stepien Grzegorz, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, and Rik Van de Walle

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.

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Published in 2016 in Proceedings of the 10th International Conference on Semantic Computing.

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Cite this article easily using its BibTeX entry: 

@inproceedings{denies_icsc_2016,
  author = {De Nies, Tom and Beecks, Christian and Godin, Fréderic and De Neve, Wesley and Grzegorz, Stepien and Arndt, D\"orthe and De Vocht, Laurens and Verborgh, Ruben and Seidl, Thomas and Mannens, Erik and Van de Walle, Rik},
  title = {Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs},
  booktitle = {Proceedings of the 10th International Conference on Semantic Computing},
  year = 2016,
  month = feb,
  pages = {254--257},
  doi = {10.1109/ICSC.2016.55},
}

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ACM
Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Stepien Grzegorz, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, and Rik Van de Walle. 2016. Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs. In Proceedings of the 10th International Conference on Semantic Computing, 254–257.
APA
De Nies, T., Beecks, C., Godin, F., De Neve, W., Grzegorz, S., Arndt, D., De Vocht, L., Verborgh, R., Seidl, T., Mannens, E., & Van de Walle, R. (2016). Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs. Proceedings of the 10th International Conference on Semantic Computing, 254–257.
IEEE
T. De Nies et al., “Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs,” in Proceedings of the 10th International Conference on Semantic Computing, 2016, pp. 254–257.
LNCS
De Nies, T., Beecks, C., Godin, F., De Neve, W., Grzegorz, S., Arndt, D., De Vocht, L., Verborgh, R., Seidl, T., Mannens, E., Van de Walle, R.: Distance-based Approach for Semantic Dissimilarity in Knowledge Graphs. In: Proceedings of the 10th International Conference on Semantic Computing. pp. 254–257 (2016).
MLA
De Nies, Tom, et al. “Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs.” Proceedings of the 10th International Conference on Semantic Computing, 2016, pp. 254–57.

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