For each subtopic relation (t i , t j ) ∈ R a , we can calculate a count/weight n(t i , t j ), representing the number of articles in A c containing the relation.
our input consists of a set of Wiki articles A c = {a}, belonging to a Wiki category c. As shown in Fig.1, each article a ∈ A c contains a CT (topic tree hierarchy) where T a is a set of topics, each denoted by a tag g and associated with a text description d g , and The output is an integrated comprehensive CTH H c = {T c , R c } where T c = {t} is a set of topics, each denoted by a set of tags t = {g} and associated by a text description d t aggregated by {d g } g∈t , and We map the problem of learning the output H c from the input {H a }, a ∈ A c , as a structure learning problem.