  Knowledge graphs (KGs) have the advantage of providing fine-grained detail
for question-answering systems. Unfortunately, building a reliable KG is
time-consuming and expensive as it requires human intervention. To overcome
this issue, we propose a novel framework to automatically construct a KG from
unstructured documents that does not require external alignment. We first
extract surface-form knowledge tuples from unstructured documents and encode
them with contextual information. Entities with similar context semantics are
then linked through internal alignment to form a graph structure. This allows
us to extract the desired information from multiple documents by traversing the
generated KG without a manual process. We examine its performance in retrieval
based QA systems by reformulating the WikiMovies and MetaQA datasets into a
tuple-level retrieval task. The experimental results show that our method
outperforms traditional retrieval methods by a large margin.
