  Question Answering (QA) models over Knowledge Bases (KBs) are capable of
providing more precise answers by utilizing relation information among
entities. Although effective, most of these models solely rely on fixed
relation representations to obtain answers for different question-related KB
subgraphs. Hence, the rich structured information of these subgraphs may be
overlooked by the relation representation vectors. Meanwhile, the direction
information of reasoning, which has been proven effective for the answer
prediction on graphs, has not been fully explored in existing work. To address
these challenges, we propose a novel neural model, Relation-updated
Direction-guided Answer Selector (RDAS), which converts relations in each
subgraph to additional nodes to learn structure information. Additionally, we
utilize direction information to enhance the reasoning ability. Experimental
results show that our model yields substantial improvements on two widely used
datasets.
