Knowledge Acquisition and Integration with Expert-in-the-loop
<|reference_start|>Knowledge Acquisition and Integration with Expert-in-the-loop: Constructing and serving knowledge graphs (KGs) is an iterative and human-centered process involving on-demand programming and analysis. In this paper, we present Kyurem, a programmable and interactive widget library that facilitates human-in-the-loop knowledge acquisition and integration to enable continuous curation a knowledge graph (KG). Kyurem provides a seamless environment within computational notebooks where data scientists explore a KG to identify opportunities for acquiring new knowledge and verify recommendations provided by AI agents for integrating the acquired knowledge in the KG. We refined Kyurem through participatory design and conducted case studies in a real-world setting for evaluation. The case-studies show that introduction of Kyurem within an existing HR knowledge graph construction and serving platform improved the user experience of the experts and helped eradicate inefficiencies related to knowledge acquisition and integration tasks<|reference_end|>
@article{rahman2024knowledge,
    title={Knowledge Acquisition and Integration with Expert-in-the-loop},
    author={Sajjadur Rahman, Frederick Choi, Hannah Kim, Dan Zhang, Estevam
  Hruschka},
    journal={arXiv preprint arXiv:2402.03291},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03291},
    primaryClass={cs.HC cs.DB}
}