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

共 50 条

[21] Joint Knowledge Graph Completion and Question Answering
Liu, Lihui
Du, Boxin
Xu, Jiejun
Xia, Yinglong
Tong, Hanghang
[J]. PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 1098 - 1108
[22] Variational Reasoning for Question Answering with Knowledge Graph
Zhang, Yuyu
Dai, Hanjun
Kozareva, Zornitsa
Smola, Alexander J.
Song, Le
[J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 6069 - 6076
[23] Knowledge Graph Embedding Based Question Answering
Huang, Xiao
Zhang, Jingyuan
Li, Dingcheng
Li, Ping
[J]. PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), 2019, : 105 - 113
[24] LAUREN - Knowledge Graph Summarization for Question Answering
Jalota, Rricha
Vollmers, Daniel
Moussallem, Diego
Ngomo, Axel-Cyrille Ngonga
[J]. 2021 IEEE 15TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2021), 2021, : 221 - 226
[25] Knowledge Graph Based Question Routing for Community Question Answering
Liu, Zhu
Li, Kan
Qu, Dacheng
[J]. NEURAL INFORMATION PROCESSING, ICONIP 2017, PT V, 2017, 10638 : 721 - 730
[26] SPARQL-QA-v2 system for Knowledge Base Question Answering
Borroto, Manuel A.
Ricca, Francesco
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 229
[27] A knowledge inference model for question answering on an incomplete knowledge graph
Guo, Qimeng
Wang, Xue
Zhu, Zhenfang
Liu, Peiyu
Xu, Liancheng
[J]. APPLIED INTELLIGENCE, 2023, 53 (07) : 7634 - 7646
[28] A knowledge inference model for question answering on an incomplete knowledge graph
Qimeng Guo
Xue Wang
Zhenfang Zhu
Peiyu Liu
Liancheng Xu
[J]. Applied Intelligence, 2023, 53 : 7634 - 7646
[29] FactQA: question answering over domain knowledge graph based on two-level query expansion
Zhang, Xiaoming
Meng, Mingming
Sun, Xiaoling
Bai, Yu
[J]. DATA TECHNOLOGIES AND APPLICATIONS, 2020, 54 (01) : 34 - 63
[30] Towards an automatic validation of answers in Question answering
Ligozat, Anne-Laure
Grau, Brigitte
Vilnat, Anne
Robba, Isabelle
Grappy, Arnaud
[J]. 19TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL II, PROCEEDINGS, 2007, : 444 - 447

← 1 2 3 4 5 →