Sim_AlMubaid_2006

Sim_AlMubaid_2006

Sim_AlMubaid_2006(
  dag,
  terms,
  distance = "longest_distances_via_LCA",
  verbose = simona_opt$verbose
)

Methods

Sim_AlMubaid_2006

It also takes accout of the distance between term a and b, and the depth of the LCA term c in the DAG. The distance is calculated as:

D(a, b) = log(1 + d(a, b)*(max_depth - depth(c)))

Here d(a, b) can be the shortest distance between a and b or the longst distance via LCA c.

Then the distance is transformed into the similarity value scaled by the possible maximal and minimal values of D(a, b) from the DAG:

D_max = log(1 + 2*max_depth * max_depth)

And the minimal value of D(a, b) is zero when a is identical to b. Then the similarity value is scaled as:

sim = 1 - D(a, b)/D_max

Paper link: doi:10.1109/IEMBS.2006.259235 .

There is a parameter distance which takes value of "longest_distances_via_LCA" (the default) or "shortest_distances_via_NCA":

term_sim(dag, terms, method = "Sim_AlMubaid_2006",
    control = list(distance = "shortest_distances_via_NCA"))