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In this paper, we investigate the Normalized Semantic Web Distance (NSWD), a semantics-aware distance measure between two concepts in a knowledge graph. Our measure advances the Normalized Web Distance, a recently established distance between two textual terms, to be more semantically aware. In addition to the theoretic fundamentals of the NSWD, we investigate its properties and qualities with respect to computation and implementation. We investigate three variants of the NSWD that make use of all semantic properties of nodes in a knowledge graph. Our performance evaluation based on the Miller-Charles benchmark shows that the NSWD is able to correlate with human similarity assessments on both Freebase and DBpedia knowledge graphs with values up to 0.69. Moreover, we verified the semantic awareness of the NSWD on a set of 20 unambiguous concept-pairs. We conclude that the NSWD is a promising measure with (1) a reusable implementation across knowledge graphs, (2) sufficient correlation with human assessments, and (3) awareness of semantic differences between ambiguous concepts.
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SIMILARITY, INFORMATION-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. “Normalized Semantic Web Distance.” In Lecture Notes in Computer Science, 9678:69–84. CHAM: SPRINGER INT PUBLISHING AG.
APA
De Nies, T., Beecks, C., Godin, F., De Neve, W., Stepien, G., Arndt, D., De Vocht, L., et al. (2016). Normalized semantic web distance. Lecture Notes in Computer Science (Vol. 9678, pp. 69–84). Presented at the 13th European Semantic Web Conference (ESWC), CHAM: SPRINGER INT PUBLISHING AG.
Vancouver
1.
De Nies T, Beecks C, Godin F, De Neve W, Stepien G, Arndt D, et al. Normalized semantic web distance. Lecture Notes in Computer Science. CHAM: SPRINGER INT PUBLISHING AG; 2016. p. 69–84.
MLA
De Nies, Tom, Christian Beecks, Fréderic Godin, et al. “Normalized Semantic Web Distance.” Lecture Notes in Computer Science. Vol. 9678. CHAM: SPRINGER INT PUBLISHING AG, 2016. 69–84. Print.
@inproceedings{8057987,
  abstract     = {In this paper, we investigate the Normalized Semantic Web Distance (NSWD), a semantics-aware distance measure between two concepts in a knowledge graph. Our measure advances the Normalized Web Distance, a recently established distance between two textual terms, to be more semantically aware. In addition to the theoretic fundamentals of the NSWD, we investigate its properties and qualities with respect to computation and implementation. We investigate three variants of the NSWD that make use of all semantic properties of nodes in a knowledge graph. Our performance evaluation based on the Miller-Charles benchmark shows that the NSWD is able to correlate with human similarity assessments on both Freebase and DBpedia knowledge graphs with values up to 0.69. Moreover, we verified the semantic awareness of the NSWD on a set of 20 unambiguous concept-pairs. We conclude that the NSWD is a promising measure with (1) a reusable implementation across knowledge graphs, (2) sufficient correlation with human assessments, and (3) awareness of semantic differences between ambiguous concepts.},
  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    = {Lecture Notes in Computer Science},
  isbn         = {978-3-319-34129-3},
  issn         = {0302-9743},
  keyword      = {SIMILARITY,INFORMATION-RETRIEVAL},
  language     = {eng},
  location     = {Heraklion, GREECE},
  pages        = {69--84},
  publisher    = {SPRINGER INT PUBLISHING AG},
  title        = {Normalized semantic web distance},
  url          = {http://dx.doi.org/10.1007/978-3-319-34129-3\_5},
  volume       = {9678},
  year         = {2016},
}

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