Question: What do feature–based methods rely on?
Evidence: Measures of semantic similarity can be classified into three broad categories : path–based, feature–based and information content (IC). Path–based similarity measures use the structure of a taxonomy to measure similarity – concepts positioned close to each other are more similar than those further apart. Feature–based methods rely on set theoretic measures of overlap between features (union and intersection). The information content measures quantify the amount of information that a concept provides – more specific concepts have a higher amount of information content.
RadaMBB89 introduce the Conceptual Distance measure. This measure is simply the length of the shortest path between two concepts ( INLINEFORM0 and INLINEFORM1 ) in the MeSH hierarchy. Paths are based on broader than (RB) and narrower than (RN) relations. CaviedesC04 extends this measure to use parent (PAR) and child (CHD) relations. Our INLINEFORM2 measure is simply the reciprocal of this shortest path value (Equation EQREF3 ), so that larger values (approaching 1) indicate a high degree of similarity. DISPLAYFORM0 
While the simplicity of INLINEFORM0 is appealing, it can be misleading when concepts are at different levels of specificity. Two very general concepts may have the same path length as two very specific concepts. WuP94 introduce a correction to INLINEFORM1 that incorporates the depth of the concepts, and the depth of their Least Common Subsumer (LCS). This is the most specific ancestor two concepts share. In this measure, similarity is twice the depth of the two concept's LCS divided by the product of the depths of the individual concepts (Equation EQREF4 ). Note that if there are multiple LCSs for a pair of concepts, the deepest of them is used in this measure. DISPLAYFORM0 
ZhongZLY02 take a very similar approach and again scale the depth of the LCS by the sum of the depths of the two concepts (Equation EQREF5 ), where INLINEFORM0 . The value of INLINEFORM1 was set to 2 based on their recommendations. DISPLAYFORM0 
PekarS02 offer another variation on INLINEFORM0 , where the shortest path of the two concepts to the LCS is used, in addition to the shortest bath between the LCS and the root of the taxonomy (Equation EQREF6 ). DISPLAYFORM0 
Feature–based methods represent each concept as a set of features and then measure the overlap or sharing of features to measure similarity. In particular, each concept is represented as the set of their ancestors, and similarity is a ratio of the intersection and union of these features.
MaedcheS01 quantify the similarity between two concepts as the ratio of the intersection over their union as shown in Equation EQREF8 . DISPLAYFORM0 
BatetSV11 extend this by excluding any shared features (in the numerator) as shown in Equation EQREF9 . DISPLAYFORM0 
Information content is formally defined as the negative log of the probability of a concept. The effect of this is to assign rare (low probability) concepts a high measure of information content, since the underlying assumption is that more specific concepts are less frequently used than more common ones.
Resnik95 modified this notion of information content in order to use it as a similarity measure. He defines the similarity of two concepts to be the information content of their LCS (Equation EQREF11 ). DISPLAYFORM0 
JiangC97, Lin98, and PirroE10 extend INLINEFORM0 by incorporating the information content of the individual concepts in various different ways. Lin98 defines the similarity between two concepts as the ratio of information content of the LCS with the sum of the individual concept's information content (Equation EQREF12 ). Note that INLINEFORM1 has the same form as INLINEFORM2 and INLINEFORM3 , and is in effect using information content as a measure of specificity (rather than depth). If there is more than one possible LCS, the LCS with the greatest IC is chosen. DISPLAYFORM0 
JiangC97 define the distance between two concepts to be the sum of the information content of the two concepts minus twice the information content of the concepts' LCS. We modify this from a distance to a similarity measure by taking the reciprocal of the distance (Equation EQREF13 ). Note that the denominator of INLINEFORM0 is very similar to the numerator of INLINEFORM1 . DISPLAYFORM0 
PirroE10 define the similarity between two concepts as the information content of the two concept's LCS divided by the sum of their individual information content values minus the information content of their LCS (Equation EQREF14 ). Note that INLINEFORM0 can be viewed as a set–theoretic version of INLINEFORM1 . DISPLAYFORM0 
Feature–based methods rely on set theoretic measures of overlap between features (union and intersection).
Answer: Feature–based methods rely on set theoretic measures of overlap between features (union and intersection).