  In this paper, we introduce the problem of knowledge graph contextualization
-- that is, given a specific NLP task, the problem of extracting meaningful and
relevant sub-graphs from a given knowledge graph. The task in the case of this
paper is the textual entailment problem, and the context is a relevant
sub-graph for an instance of the textual entailment problem -- where given two
sentences p and h, the entailment relationship between them has to be predicted
automatically. We base our methodology on finding paths in a cost-customized
external knowledge graph, and building the most relevant sub-graph that
connects p and h. We show that our path selection mechanism to generate
sub-graphs not only reduces noise, but also retrieves meaningful information
from large knowledge graphs. Our evaluation shows that using information on
entities as well as the relationships between them improves on the performance
of purely text-based systems.
