  Recent advancements in Large Language Models (LLMs) have showcased their
proficiency in answering natural language queries. However, their effectiveness
is hindered by limited domain-specific knowledge, raising concerns about the
reliability of their responses. We introduce a hybrid system that augments LLMs
with domain-specific knowledge graphs (KGs), thereby aiming to enhance factual
correctness using a KG-based retrieval approach. We focus on a medical KG to
demonstrate our methodology, which includes (1) pre-processing, (2) Cypher
query generation, (3) Cypher query processing, (4) KG retrieval, and (5)
LLM-enhanced response generation. We evaluate our system on a curated dataset
of 69 samples, achieving a precision of 78\% in retrieving correct KG nodes.
Our findings indicate that the hybrid system surpasses a standalone LLM in
accuracy and completeness, as verified by an LLM-as-a-Judge evaluation method.
This positions the system as a promising tool for applications that demand
factual correctness and completeness, such as target identification -- a
critical process in pinpointing biological entities for disease treatment or
crop enhancement. Moreover, its intuitive search interface and ability to
provide accurate responses within seconds make it well-suited for
time-sensitive, precision-focused research contexts. We publish the source code
together with the dataset and the prompt templates used.
