  Knowledge graph based simple question answering (KBSQA) is a major area of
research within question answering. Although only dealing with simple
questions, i.e., questions that can be answered through a single knowledge base
(KB) fact, this task is neither simple nor close to being solved. Targeting on
the two main steps, subgraph selection and fact selection, the research
community has developed sophisticated approaches. However, the importance of
subgraph ranking and leveraging the subject--relation dependency of a KB fact
have not been sufficiently explored. Motivated by this, we present a unified
framework to describe and analyze existing approaches. Using this framework as
a starting point, we focus on two aspects: improving subgraph selection through
a novel ranking method and leveraging the subject--relation dependency by
proposing a joint scoring CNN model with a novel loss function that enforces
the well-order of scores. Our methods achieve a new state of the art (85.44% in
accuracy) on the SimpleQuestions dataset.
