This paper focuses on mining the hyponymy (or is-a) relation from large-scale, open-domain web documents. A nonlinear probabilistic model is exploited to model the correlation between sentences in the aggregation of pattern matching results. Based on the model, we design a set of evidence combination and propagation algorithms. These significantly improve the result quality of existing approaches. Ex-perimental results conducted on 500 mil-lion web pages and hypernym labels for 300 terms show over 20% performance improvement in terms of P@5, MAP and R-Precision.