  Knowledge graphs (KGs) are large datasets with specific structures
representing large knowledge bases (KB) where each node represents a key entity
and relations amongst them are typed edges. Natural language queries formed to
extract information from a KB entail starting from specific nodes and reasoning
over multiple edges of the corresponding KG to arrive at the correct set of
answer nodes. Traditional approaches of question answering on KG are based on
(a) semantic parsing (SP), where a logical form (e.g., S-expression, SPARQL
query, etc.) is generated using node and edge embeddings and then reasoning
over these representations or tuning language models to generate the final
answer directly, or (b) information-retrieval based that works by extracting
entities and relations sequentially. In this work, we evaluate the capability
of (LLMs) to answer questions over KG that involve multiple hops. We show that
depending upon the size and nature of the KG we need different approaches to
extract and feed the relevant information to an LLM since every LLM comes with
a fixed context window. We evaluate our approach on six KGs with and without
the availability of example-specific sub-graphs and show that both the IR and
SP-based methods can be adopted by LLMs resulting in an extremely competitive
performance.
