Measures

# Similarity Measures

## Path length

A simple node-counting scheme (path). The similarity score is inversely proportional to the number of nodes along the shortest path between the concepts. The shortest possible path occurs when the two concepts are the same, in which case the length is 1. Thus, the maximum similarity value is 1.

## Leacock & Chodorow

The similarity measure proposed by Leacock and Chodorow (lch) is -log (length / (2 * D)), where length is the length of the shortest path between the two concepts (using node-counting) and D is the maximum depth of the taxonomy.

## Wu & Palmer

The Wu & Palmer measure (wup) calculates similarity by considering the depths of the two concepts in the UMLS, along with the depth of the LCS The formula is score = 2*depth(lcs) / (depth(s1) + depth(s2)). This means that 0 < score <= 1. The score can never be zero because the depth of the LCS is never zero (the depth of the root of a taxonomy is one). The score is one if the two input concepts are the same.

## Resnik

The related value is equal to the information content (IC) of the Least Common Subsumer (LCS) (most informative subsumer). This means that the value will always be greater-than or equal-to zero. The upper bound on the value is generally quite large and varies depending upon the size of the corpus used to determine information content values. To be precise, the upper bound should be ln (N) where N is the number of words in the corpus.

## Jiang & Conrath

The similarity value returned by the jcn measure is equal to 1 / jcn_distance, where jcn_distance is equal to IC(concept1) + IC(concept2) - 2 * IC(lcs).

There are two special cases that need to be handled carefully when computing similarity; both of these involve the case when jcn_distance is zero.

In the first case, we have ic(concept1) = ic(concept2) = ic(lcs) = 0. In an ideal world, this would only happen when all three concepts, viz. concept1, concept2, and lcs, are the root node. However, when a concept has a frequency count of zero, we use the value 0 for the information content. In this first case, we return 0 due to lack of data.

In the second case, we have ic(concept1) + ic(concept2) = 2 * ic(ics). This is almost always found when concept1 = concept2 = lcs (i.e., the two input concepts are the same). Intuitively this is the case of maximum similarity, which would be infinity, but it is impossible to return infinity. Insteady we find the smallest possible distance greater than zero and return the multiplicative inverse of that distance.

## Lin

The similarity value returned by the lin measure is a number equal to 2 * IC(lcs) / (IC(concept1) + IC(concept2)). Where IC(x) is the information content of x. One can observe, then, that the similarity value will be greater-than or equal-to zero and less-than or equal-to one.

If the information content of any of either concept1 or concept2 is zero, then zero is returned as the similarity score, due to lack of data. Ideally, the information content of a concept would be zero only if that concept were the root node, but when the frequency of a concept is zero, we use the value of zero as the information content because of a lack of better alternatives.

## Conceptual Distance

The similarity value returned by the cdist measure is the number of edges between two concepts in the UMLS. This was initial proposed by Rada, et. al. using the RB/RN relations in the UMLS and later evaluated by Caviedes and Cimino using the PAR/CHD relations.

## Nguyen and Al-Mubaid

The similarity value returned by the nam measure is computed by calculating the log of two plus the product of the shortest distance between the two concepts minus one and the depth of the taxonomy minus the depth of the LCS. Concepts that are more similar with have a lower similarity score than concepts that are less similar with this measure.

## Random

The similarity values are simply randomly generated numbers. This is intended only to be used as a baseline.