  Knowledge Base Question Answering (KBQA) tasks that in-volve complex
reasoning are emerging as an important re-search direction. However, most KBQA
systems struggle withgeneralizability, particularly on two dimensions: (a)
acrossmultiple reasoning types where both datasets and systems haveprimarily
focused on multi-hop reasoning, and (b) across mul-tiple knowledge bases, where
KBQA approaches are specif-ically tuned to a single knowledge base. In this
paper, wepresent SYGMA, a modular approach facilitating general-izability
across multiple knowledge bases and multiple rea-soning types. Specifically,
SYGMA contains three high levelmodules: 1) KB-agnostic question understanding
module thatis common across KBs 2) Rules to support additional reason-ing types
and 3) KB-specific question mapping and answeringmodule to address the
KB-specific aspects of the answer ex-traction. We demonstrate effectiveness of
our system by evalu-ating on datasets belonging to two distinct knowledge
bases,DBpedia and Wikidata. In addition, to demonstrate extensi-bility to
additional reasoning types we evaluate on multi-hopreasoning datasets and a new
Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in
thispaper. We show that our generalizable approach has bettercompetetive
performance on multiple datasets on DBpediaand Wikidata that requires both
multi-hop and temporal rea-soning
