A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Structured query construction via knowledge graph embedding (2020)

Wang, R., Wang, M., Liu, J., Cochez, M., & Decker, S. (2020). Structured query construction via knowledge graph embedding. Knowledge and Information Systems, 62(5), 1819-1846. https://doi.org/10.1007/s10115-019-01401-x

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Wang, Ruijie; Wang, Meng; Liu, Jun; Cochez, Michael; Decker, Stefan

Lehti tai sarja: Knowledge and Information Systems

ISSN: 0219-1377

eISSN: 0219-3116

Julkaisuvuosi: 2020

Volyymi: 62

Lehden numero: 5

Artikkelin sivunumerot: 1819-1846

Kustantaja: Springer

Kustannuspaikka: Lontoo

Julkaisumaa: Britannia

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/67172

Rinnakkaistallenteen verkko-osoite (pre-print): https://arxiv.org/abs/1909.02930

Tiivistelmä

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.

YSO-asiasanat: kyselykielet; luonnollinen kieli; tietomallit

Vapaat asiasanat: knowledge graph; query construction; knowledge graph embedding; natural language question answering

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 2