@inproceedings{yao-etal-2023-viskop,
title = "{V}is{K}o{P}: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering",
author = "Yao, Zijun and
Chen, Yuanyong and
Lv, Xin and
Cao, Shulin and
Xin, Amy and
Yu, Jifan and
Jin, Hailong and
Xu, Jianjun and
Zhang, Peng and
Hou, Lei and
Li, Juanzi",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.17",
doi = "10.18653/v1/2023.acl-demo.17",
pages = "179--189",
abstract = "We present Visual Knowledge oriented Programming platform (\textbf{VisKoP}), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements. KoPL programs can be edited with simple graphical operators, such as \textit{{''}dragging{''}} to add knowledge operators and \textit{{''}slot filling{''}} to designate operator arguments. Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results. To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end. Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. The VisKoP online demo, highly efficient KoPL engine, and screencast video are now publicly available.",
}
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<abstract>We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements. KoPL programs can be edited with simple graphical operators, such as ”dragging” to add knowledge operators and ”slot filling” to designate operator arguments. Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results. To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end. Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. The VisKoP online demo, highly efficient KoPL engine, and screencast video are now publicly available.</abstract>
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%0 Conference Proceedings
%T VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
%A Yao, Zijun
%A Chen, Yuanyong
%A Lv, Xin
%A Cao, Shulin
%A Xin, Amy
%A Yu, Jifan
%A Jin, Hailong
%A Xu, Jianjun
%A Zhang, Peng
%A Hou, Lei
%A Li, Juanzi
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yao-etal-2023-viskop
%X We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements. KoPL programs can be edited with simple graphical operators, such as ”dragging” to add knowledge operators and ”slot filling” to designate operator arguments. Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results. To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end. Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. The VisKoP online demo, highly efficient KoPL engine, and screencast video are now publicly available.
%R 10.18653/v1/2023.acl-demo.17
%U https://aclanthology.org/2023.acl-demo.17
%U https://doi.org/10.18653/v1/2023.acl-demo.17
%P 179-189
Markdown (Informal)
[VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering](https://aclanthology.org/2023.acl-demo.17) (Yao et al., ACL 2023)
ACL
- Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, and Juanzi Li. 2023. VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 179–189, Toronto, Canada. Association for Computational Linguistics.