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
收藏
页码：78 / 82
页数：5
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