@inproceedings{nguyen-etal-2021-inside,
    title = "Inside {ASCENT}: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering",
    author = "Nguyen, Tuan-Phong  and
      Razniewski, Simon  and
      Weikum, Gerhard",
    editor = "Ji, Heng  and
      Park, Jong C.  and
      Xia, Rui",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.acl-demo.5/",
    doi = "10.18653/v1/2021.acl-demo.5",
    pages = "40--47",
    abstract = "ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website (\url{https://ascent.mpi-inf.mpg.de}) and an introductory video (\url{https://youtu.be/qMkJXqu_Yd4}) are both available online."
}