  Fact-based Visual Question Answering (FVQA), a challenging variant of VQA,
requires a QA-system to include facts from a diverse knowledge graph (KG) in
its reasoning process to produce an answer. Large KGs, especially common-sense
KGs, are known to be incomplete, i.e. not all non-existent facts are always
incorrect. Therefore, being able to reason over incomplete KGs for QA is a
critical requirement in real-world applications that has not been addressed
extensively in the literature. We develop a novel QA architecture that allows
us to reason over incomplete KGs, something current FVQA state-of-the-art
(SOTA) approaches lack.We use KG Embeddings, a technique widely used for KG
completion, for the downstream task of FVQA. We also employ a new image
representation technique we call "Image-as-Knowledge" to enable this
capability, alongside a simple one-step co-Attention mechanism to attend to
text and image during QA. Our FVQA architecture is faster during inference
time, being O(m), as opposed to existing FVQA SOTA methods which are O(N logN),
where m is number of vertices, N is number of edges (which is O(m^2)). We
observe that our architecture performs comparably in the standard
answer-retrieval baseline with existing methods; while for missing-edge
reasoning, our KG representation outperforms the SOTA representation by 25%,
and image representation outperforms the SOTA representation by 2.6%.
