  Question answering (QA) over knowledge bases (KBs) is challenging because of
the diverse, essentially unbounded, types of reasoning patterns needed.
However, we hypothesize in a large KB, reasoning patterns required to answer a
query type reoccur for various entities in their respective subgraph
neighborhoods. Leveraging this structural similarity between local
neighborhoods of different subgraphs, we introduce a semiparametric model
(CBR-SUBG) with (i) a nonparametric component that for each query, dynamically
retrieves other similar $k$-nearest neighbor (KNN) training queries along with
query-specific subgraphs and (ii) a parametric component that is trained to
identify the (latent) reasoning patterns from the subgraphs of KNN queries and
then apply them to the subgraph of the target query. We also propose an
adaptive subgraph collection strategy to select a query-specific compact
subgraph, allowing us to scale to full Freebase KB containing billions of
facts. We show that CBR-SUBG can answer queries requiring subgraph reasoning
patterns and performs competitively with the best models on several KBQA
benchmarks. Our subgraph collection strategy also produces more compact
subgraphs (e.g. 55\% reduction in size for WebQSP while increasing answer
recall by 4.85\%)\footnote{Code, model, and subgraphs are available at
\url{https://github.com/rajarshd/CBR-SUBG}}.
