1. A method to perform relation extraction in text, comprising: applying a convolution strategy to determine a kernel between sentences; deriving an unweighted undirected graph G D (S) for a sentence S from a set of dependency relations supplemented by a linear-order structure, where the set is denoted by D(S) and V(S) is the set of vertices, with each v i εV(S) representing a certain word 
 G D ( S )=( V ( S ), E ( S )); determining a single path p from a dependency graph G D (S) composed from a sequence of words and their associated dependencies 
 p =( w i ,d i,j ,w j ,. .. ,w p ,d p,q ,w q ) where word w i and w j are connected by the dependency edge d i,j ; determining a convolution kernel K G as a sum of kernels on paths (K p ): K G ⁡ ( G D ⁡ ( S ) , G D ⁡ ( S ′ ) ) = ∑ p ∈ P n ⁡ ( G D ⁡ ( S ) ) ⁢ ⁢ ∑ p ′ ∈ P ″ ⁡ ( G D ⁡ ( S ′ ) ) ⁢ ⁢ K p ⁡ ( p , p ′ ) ⁢ Pr ⁡ ( p ⁢  G D ⁡ ( S ) ) ⁢ Pr ( p ′  ⁢ G D ⁡ ( S ′ ) ) where Pr(p|G D (S)) is a probability that single path p happens in the graph G D (S) and calculated as a ratio of path count over sum of path counts; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.