  Textual entailment is a fundamental task in natural language processing. Most
approaches for solving the problem use only the textual content present in
training data. A few approaches have shown that information from external
knowledge sources like knowledge graphs (KGs) can add value, in addition to the
textual content, by providing background knowledge that may be critical for a
task. However, the proposed models do not fully exploit the information in the
usually large and noisy KGs, and it is not clear how it can be effectively
encoded to be useful for entailment. We present an approach that complements
text-based entailment models with information from KGs by (1) using
Personalized PageR- ank to generate contextual subgraphs with reduced noise and
(2) encoding these subgraphs using graph convolutional networks to capture KG
structure. Our technique extends the capability of text models exploiting
structural and semantic information found in KGs. We evaluate our approach on
multiple textual entailment datasets and show that the use of external
knowledge helps improve prediction accuracy. This is particularly evident in
the challenging BreakingNLI dataset, where we see an absolute improvement of
5-20% over multiple text-based entailment models.
