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.

中文翻译：

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通过知识图嵌入的结构化查询构造

为了促进普通用户访问知识图，正在加大努力来构造给定自然语言问题的图结构查询。构造的核心是推导目标查询的结构，并确定构成查询的顶点/边。现有的查询构造方法依赖于问题理解和基于传统图的算法，这导致面向知识图的大规模自然语言面临复杂自然语言问题的效率低下和性能下降。在本文中，我们着眼于这个问题，并提出了一个基于最新知识图嵌入技术的新颖框架。我们的框架首先通过利用广义局部知识图将基础知识图编码为低维嵌入空间。给定自然语言问题，知识图的学习嵌入表示将用于计算查询结构并将顶点/边组合到目标查询中。在基准数据集上进行了广泛的实验，结果表明我们的框架在有效性和效率方面优于最新的基线模型。