Structured Query Construction via Knowledge Graph Embedding

TL;DR

This paper introduces a novel framework that uses knowledge graph embeddings to improve the construction of structured queries from natural language questions, enhancing efficiency and effectiveness over existing methods.

Contribution

The paper presents a new approach leveraging knowledge graph embeddings to better deduce query structures from natural language questions, outperforming traditional graph-based algorithms.

Findings

Outperforms baseline models in effectiveness.

Achieves higher efficiency in query construction.

Demonstrates robustness on benchmark datasets.

Abstract

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.