@inproceedings{saxena-etal-2021-leveraging,
    title = "Leveraging {W}ikipedia Navigational Templates for Curating Domain-Specific Fuzzy Conceptual Bases",
    author = "Saxena, Krati  and
      Singh, Tushita  and
      Patil, Ashwini  and
      Sunkle, Sagar  and
      Kulkarni, Vinay",
    booktitle = "Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.dash-1.1",
    doi = "10.18653/v1/2021.dash-1.1",
    pages = "1--7",
    abstract = "Domain-specific conceptual bases use key concepts to capture domain scope and relevant information. Conceptual bases serve as a foundation for various downstream tasks, including ontology construction, information mapping, and analysis. However, building conceptual bases necessitates domain awareness and takes time. Wikipedia navigational templates offer multiple articles on the same/similar domain. It is possible to use the templates to recognize fundamental concepts that shape the domain. Earlier work in this domain used Wikipedia{'}s structured and unstructured data to construct open-domain ontologies, domain terminologies, and knowledge bases. We present a novel method for leveraging navigational templates to create domain-specific fuzzy conceptual bases in this work. Our system generates knowledge graphs from the articles mentioned in the template, which we then process using Wikidata and machine learning algorithms. We filter important concepts using fuzzy logic on network metrics to create a crude conceptual base. Finally, the expert helps by refining the conceptual base. We demonstrate our system using an example of RNA virus antiviral drugs.",
}