@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.",
}

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.
Yao, Zijun Chen, Yuanyong Lv, Xin Cao, Shulin Xin, Amy Yu, Jifan Jin, Hailong Xu, Jianjun Zhang, Peng Hou, Lei Li, Juanzi
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering