Towards Knowledge Graph-Agnostic SPARQL Query Validation for Improving Question Answering

被引：0

作者：

Perevalov, Aleksandr ^{[1
,2
]}

Gashkov, Aleksandr ^{[3
]}

Eltsova, Maria ^{[3
]}

Both, Andreas ^{[2
,4
]}

机构：

[1] Anhalt Univ Appl Sci, Kothen, Germany

[2] Leipzig Univ Appl Sci, Leipzig, Germany

[3] Perm Natl Res Polytech Univ, Perm, Russia

[4] DATEV EG, Nurnberg, Germany

来源：

SEMANTIC WEB: ESWC 2022 SATELLITE EVENTS | 2022年 / 13384卷

关键词：

Question answering; Knowledge graphs; Query validation; Query ranking; API; Demonstrator;

D O I：

10.1007/978-3-031-11609-4_15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Knowledge Graph Question Answering (KGQA) system needs to generate a SPARQL query over a knowledge graph (KG) that is reflecting a user's information need expressed by the given natural-language question. Yet, many of these generated queries might be completely mismatching. To deal with this problem, we developed a KG-agnostic approach that is intended to increase the KGQA quality while validating SPARQL query candidates and finally removing the incorrect ones. In this demonstration, we provide the research community a Web user interface and a RESTful API to experiment with the processing of our approach and experience the possible impact of such an approach.

引用

页码：78 / 82

页数：5

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