VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge
  Base Question Answering
Zijun Yao Yuanyong Chen Xin Lv Shulin Cao Amy Xin Jifan Yu Hailong Jin Jianjun Xu Peng Zhang Lei Hou Juanzi Li
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
https://demoviskop.xlore.cn (Stable release of this paper) and
https://viskop.xlore.cn (Beta release with new features), highly efficient KoPL
engine https://pypi.org/project/kopl-engine, and screencast video
https://youtu.be/zAbJtxFPTXo are now publicly available.