To estimate each pattern's importance score, we suggest that a context pattern is important to the semantic of an entity if: the pattern matches the entity frequently; the pattern matches as few other entities as possible.
we design a importance score function for each context pattern p of an entity e as follows: where C(p) is the number of different entities matched by p, N (e, p) is the frequency of p i matching entity e. Ultimately, the above importance scores are mapped to the distributional probabilities of each entity's context pattern such that all probabilities sum up to 1.