# Semantic Similarity Based on Taxonomies
## 6 Discussion  
In Section 3, the methods for computing the semantic similarity between concepts in a taxonomy have been introduced and summarized in Table 3. They have been partitioned into methods relying on the IC and methods that do not use the IC. All the methods based on the IC for computing similarity between concepts consider the least common subsumer (_lcs_) of the compared concepts. In particular, they consider the IC of the _lcs_, i.e., the amount of informativeness shared by the two concepts. In fact, in \(sim_{res}\), the contributing hypernym is also the _lcs_. In the \(D_{k}\) similarity, the _lcs_ does not appear explicitly in the formula, but since the method uses an IC-based function, it exploits this feature, indirectly. Therefore, the IC of the _lcs_ can be recognized as the characteristic typical of these methods. Besides the _[PERSON] and [PERSON] similarity_[24] and the _wpath similarity_[38], which address the _len_ of the compared concepts, and the _hierarchical semantic similarity_[40], which considers hyponyms, the other IC-based methods do not use further features. This can be explained because, indeed, the properties of the taxonomy are used in the computation of the IC. Furthermore, it is worth mentioning that the [PERSON] similarity [35] has inspired at least two other methods, which are the _[PERSON] and [PERSON] similarity_[24] and the _Zhang 1 similarity_[33]. In fact, the former, according to Equation (5), is [PERSON]'s formulation multiplied by an expression that is inversely proportional to the _len_ between the compared concepts, whereas the latter, according to Equation (7) is [PERSON]'s formulation re-scaled by the logarithm function. Analogously, the methods that are not based on the IC notion mainly consider the _lcs_ between the compared concepts, even if in different manners. In particular, the _[PERSON] and [PERSON] similarity_[42], _[PERSON] similarity_[49], _[PERSON] similarity_[45], and _[PERSON] and [PERSON] similarity_[46] consider the depth of the _lcs_, whereas the _Zhang 2 similarity_[47] considers the number of hyponyms of the _lcs_. The _[PERSON] and [PERSON] similarity_ can be considered as the non-IC version of the _[PERSON] similarity_. In fact, the former resembles the latter but considers the depth instead of the IC. Furthermore, in the case the taxonomy is a tree, this similarity metric is equivalent to the _[PERSON] and [PERSON] similarity_. In fact, in a tree-shaped taxonomy, given two concepts \(c_{1}\) and \(c_{2}\), \(depth(c_{1})\) and \(depth(c_{2})\) are equal to \(depth(lcs(c_{1},c_{2}))+len(c_{1},lcs(c_{1},c_{2}))\) and \(depth(lcs(c_{1},c_{2}))+len(c_{2},lcs(c_{1},c_{2}))\), respectively. In Section 4, some of the most representative methods for computing taxonomy-based semantic similarity between sets of concepts were illustrated. In particular, _WNSim_[53], the method defined by [PERSON] and [PERSON] [46], the one introduced by [PERSON] et al. [54], the measure proposed by [PERSON] et al. [55], and \(SemSim^{p}\)[18]. Figure 1 shows the dependencies among the recalled methods. In the following, the experimentation presented in [18] is briefly recalled and updated by including the latest results, as explained below. The evaluation has been performed by carrying out two experiments. They are based on a taxonomy derived from the ACM Computing Classification System (CCS) and a large dataset of about \(1K\) articles from the ACM Digital Library, each associated with an annotation vector of concepts from such a taxonomy. In general, the semantic similarity between concepts is evaluated by asking a group of people to express similarity values between pairs of concepts belonging to _golden datasets_ (such as for instance \(M\&C\)[57], \(R122\)[58], and \(R\&G\)[59]), which represent benchmarks in the evaluation and comparison of different similarity methods.