Description Usage Arguments Details Value Examples

This method shows the value of the pairwise configuration.

and configures the pairwise measure to compute semantic similarity between two concepts of a given ontology.To set the pairwise measure one of the available short flags described in details should be used.

1 2 3 4 5 6 7 8 9 | ```
pairwiseConfig(object)
pairwiseConfig(object) <- value
## S4 method for signature 'Similarity'
pairwiseConfig(object)
## S4 replacement method for signature 'Similarity'
pairwiseConfig(object) <- value
``` |

`object` |
instance of class |

`value` |
See details |

The following measures can be used to compute semantic similarities between two concepts.

'edge_rada_lca' : Computes the similarity of two concepts based on the shortest path linking the two concepts.

*sim(u,v) = 1 /sp(u,v)*'edge_wupalmer': Computes the similarity of two concepts based on the depth of the concepts and the depth of their most specific common ancestor

*sim(u,v) = depth(MSCA[u,v]) / (depth(u) + depth(v))*'edge_resnik': Computes the similarity of two concepts based on the shortest path between the concepts and the maximum depth of the taxonomy

*(2 * max_depth - min_sp(u,v)) / (2 * max_depth)*max_depth is the maximum depth in the ontology

sp(u,v) is the shortest path legnth between u and v

'edge_leachod': Computes the similarity of two concepts based on the shortest path as Rada but also considering the depth of the ontology

*sim(u,v) = -log( (sp(u,v) + 1) / 2 * max_depth )*'edge_slimani': Computes the similarity of two concepts based on the depth of the most specific common ancesto and the max depth of the concepts

*sim(u,v) = 2 * depth(MCA) / ((depth(u) + depth(v) + 1) * pf ))*depth(MCA) is the maximum depth of the most common ancestor of the concepts

pf is a penalization factor used when concepts belong to the same hierarchy

The following measures require the specification of an additional meausre to compute the information content of nodes.

'lin': Computes the similarity between two concepts based on the information content of the two concepts and the information content of the most informative common ancestor of the two concepts

*sim(u, v) = (2 * IC(MICA)) / ( IC(u) + IC(v) )*IC(MICA) is the information content of the most informative common ancestor of u and v. MICA is the concept in the ancestors of both u and v that maximizes the Information Content measure.

'resnik': Computes the similarity between two concepts based on the information content of the most informative common ancestors of the compared concepts

*sim(u,v) = IC(MICA)*'schlicker': Computes the similarity between two concepts based on the information concent of the most informative common ancestor of the compared concepts and its probability of occurrence

*sim(u,v) = (2 * IC(MICA)) / ( IC(u) + IC(v)) * (1 - Prob_MICA)*Prob_MICA is the probability of occurrence of the most informative common ancestor of the compared concepts

'jaccard': Computes the similarity between two concepts based on the information content of the most informative common ancestor.

*sim(u, v) = IC(MICA) / (IC(u) + IC(v) - IC(MICA))*if the sum of the IC of the concepts is different from the IC of the MICA else sim(u, v) = 0.'sim': This measure is based on

`lin`

similarity*sim(u, v) = lin(u, v) - (1 - (1 / (1+ IC(MICA))))*'jc_norm': Computes the similarity between two concepts based on the IC of the most informative ancestor of the concpets

*sim(u,v) = 1 - (IC(u) + IC(v) - 2 * IC(MICA)) / 2*

Information content based measures require the configuration parameter for estimating concept specificity. Intrinsic estimation uses the topological properties of the taxonomic backbone of the semantic graph. There are different options:

'zhou': Intrinsic estimation of the specificity of the concepts based on their depth in the ontology.

*IC(c) = k( 1 - log(D(c))/log(|C|)) + (1 - k) (log(max(depth(x)))/ log(depth_max))*k is a factor to adjust the weight of the two items of the equation

D(c) is the number of hyponims of concept c

|C| is the number of concepts in the ontology

depth(c) is the maximum depth of concept c

depth_max is the maximum depth in the ontology

'resnik_1995': Intrinsic estimation of the specificity of concepts based on the number of ancestors of the concept.

*IC(c) = |A(c)|*'seco'Intrinsic estimation of the specificity of the concepts based on the number of concepts they subsume.

*IC(c) = 1 - ( log(D(c) / log(|C|) )*D(c) is the number of hyponims of concept c

|C| is the number of concepts in the ontology

'sanchez': Intrinsic estimation of the specificity of the concepts based on the number of leaves and the number of subsumers of the concepts

*IC(c) = -log(x / nb_leaves + 1)*with*x = |leaves(c)| / |A(c)|*nb_leaves is the represents the number of leaves corresponding to the root node of the hierarchy

leaves(c) is the number of leaves corresponding to the concept c

|A(c)| is the number of concepts that subsume c

'anc_norm': Intrinsic estimation of the specificity of concepts based on the number of ancestors of a given concept normalized on the number of concepts in the ontology.

'depth_min_non_linear': Intrinsic estimation of the specificity of concepts based on their minimum depth.

'depth_max_non_linear': Intrinsic estimation of the specificity of concepts based on their maximum depth.

The pairwise measure

instance of the Similarity class with the new pairwise option.

1 2 3 4 5 6 7 8 9 10 | ```
sim <- new('Similarity')
obo <- system.file('extdata', 'sample.cs.obo', package='OnassisJavaLibs')
ontology(sim) <- obo
pairwiseConfig(sim)
sim <- new('Similarity')
obo <- system.file('extdata', 'sample.cs.obo', package='OnassisJavaLibs')
ontology(sim) <- obo
pairwiseConfig(sim) <- 'edge_resnik'
#The following configuration uses an information content based measure
pairwiseConfig(sim) <- c('resnik', 'seco')
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.