  Biomedical knowledge graphs (KG) are heterogenous networks consisting of
biological entities as nodes and relations between them as edges. These
entities and relations are extracted from millions of research papers and
unified in a single resource. The goal of biomedical multi-hop
question-answering over knowledge graph (KGQA) is to help biologist and
scientist to get valuable insights by asking questions in natural language.
Relevant answers can be found by first understanding the question and then
querying the KG for right set of nodes and relationships to arrive at an
answer. To model the question, language models such as RoBERTa and BioBERT are
used to understand context from natural language question. One of the
challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE)
help to overcome this problem by encoding nodes and edges in a dense and more
efficient way. In this paper, we use a publicly available KG called Hetionet
which is an integrative network of biomedical knowledge assembled from 29
different databases of genes, compounds, diseases, and more. We have enriched
this KG dataset by creating a multi-hop biomedical question-answering dataset
in natural language for testing the biomedical multi-hop question-answering
system and this dataset will be made available to the research community. The
major contribution of this research is an integrated system that combines
language models with KG embeddings to give highly relevant answers to free-form
questions asked by biologists in an intuitive interface. Biomedical multi-hop
question-answering system is tested on this data and results are highly
encouraging.
