  In order to facilitate the accesses of general users to knowledge graphs, an
increasing effort is being exerted to construct graph-structured queries of
given natural language questions. At the core of the construction is to deduce
the structure of the target query and determine the vertices/edges which
constitute the query. Existing query construction methods rely on question
understanding and conventional graph-based algorithms which lead to inefficient
and degraded performances facing complex natural language questions over
knowledge graphs with large scales. In this paper, we focus on this problem and
propose a novel framework standing on recent knowledge graph embedding
techniques. Our framework first encodes the underlying knowledge graph into a
low-dimensional embedding space by leveraging generalized local knowledge
graphs. Given a natural language question, the learned embedding
representations of the knowledge graph are utilized to compute the query
structure and assemble vertices/edges into the target query. Extensive
experiments were conducted on the benchmark dataset, and the results
demonstrate that our framework outperforms state-of-the-art baseline models
regarding effectiveness and efficiency.
