A1 Journal article (refereed)
Structured query construction via knowledge graph embedding (2020)


Wang, Ruijie; Wang, Meng; Liu, Jun; Cochez, Michael; Decker, Stefan (2020). Structured query construction via knowledge graph embedding. Knowledge and Information Systems, 62 (5), 1819-1846. DOI: 10.1007/s10115-019-01401-x


JYU authors or editors


Publication details

All authors or editors: Wang, Ruijie; Wang, Meng; Liu, Jun; Cochez, Michael; Decker, Stefan

Journal or series: Knowledge and Information Systems

ISSN: 0219-1377

eISSN: 0219-3116

Publication year: 2020

Volume: 62

Issue number: 5

Pages range: 1819-1846

Publisher: Springer

Place of Publication: Lontoo

Publication country: United Kingdom

Publication language: English

DOI: http://doi.org/10.1007/s10115-019-01401-x

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67172

Web address of parallel published publication (pre-print): https://arxiv.org/abs/1909.02930


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.


Keywords: query languages; natural language; data models

Free keywords: knowledge graph; query construction; knowledge graph embedding; natural language question answering


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 2


Last updated on 2020-18-08 at 13:14