  We present a question answering system over DBpedia, filling the gap between
user information needs expressed in natural language and a structured query
interface expressed in SPARQL over the underlying knowledge base (KB). Given
the KB, our goal is to comprehend a natural language query and provide
corresponding accurate answers. Focusing on solving the non-aggregation
questions, in this paper, we construct a subgraph of the knowledge base from
the detected entities and propose a graph traversal method to solve both the
semantic item mapping problem and the disambiguation problem in a joint way.
Compared with existing work, we simplify the process of query intention
understanding and pay more attention to the answer path ranking. We evaluate
our method on a non-aggregation question dataset and further on a complete
dataset. Experimental results show that our method achieves best performance
compared with several state-of-the-art systems.
