  Knowledge Graphs (KG) act as a great tool for holding distilled information
from large natural language text corpora. The problem of natural language
querying over knowledge graphs is essential for the human consumption of this
information. This problem is typically addressed by converting the natural
language query to a structured query and then firing the structured query on
the KG. Direct answering models over knowledge graphs in literature are very
few. The query conversion models and direct models both require specific
training data pertaining to the domain of the knowledge graph. In this work, we
convert the problem of natural language querying over knowledge graphs to an
inference problem over premise-hypothesis pairs. Using trained deep learning
models for the converted proxy inferencing problem, we provide the solution for
the original natural language querying problem. Our method achieves over 90%
accuracy on MetaQA dataset, beating the existing state-of-the-art. We also
propose a model for inferencing called Hierarchical Recurrent Path
Encoder(HRPE). The inferencing models can be fine-tuned to be used across
domains with less training data. Our approach does not require large
domain-specific training data for querying on new knowledge graphs from
different domains.
