Edge-based Semantic Similarity Measure

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An Edge-based Semantic Similarity Measure is a Topological Semantic Similarity Measure that calculates the similarity between ontological concepts by counting edges in a semantic network.



References

2021

  • (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_similarity#Topological_similarity Retrieved:2021-8-7.
    • There are essentially two types of approaches that calculate topological similarity between ontological concepts:
      • Edge-based: which use the edges and their types as the data source;
      • Node-based: in which the main data sources are the nodes and their properties.
    • Other measures calculate the similarity between ontological instances:
      • Pairwise: measure functional similarity between two instances by combining the semantic similarities of the concepts they represent
      • Groupwise: calculate the similarity directly not combining the semantic similarities of the concepts they represent

2010

2009

  1. Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets. 1989. pp. 17–30. In: IEEE Transaction on Systems, Man, and Cybernetics. 19.
  2. Wu Z, Palmer MS. Verb semantics and lexical selection. Proceedings of the 32nd. Annual Meeting of the Association for Computational Linguistics (ACL 1994) 1994. pp. 133–138. URL http://dblp.uni-trier.de/db/conf/acl/acl94.html#WuP94.

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$T(a, b)=\dfrac{\delta(\operatorname{root}, c)}{\delta(a, c)+\delta(b, c)+\delta(root, c)}$

(2)
where $c = lcs(a,b)$. $T$ is such that $0\leq T \leq 1$, with 1 standing for the maximum taxonomic similarity.

$T$ is directly proportional to the number of edges from the least common super-concept to the root, which agrees with the intuition that a given number of edges between two concrete concepts signifies greater similarity than the same number of edges between two abstract concepts.