About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Data and Information in Online Environments. Second EAI International Conference, DIONE 2021, Virtual Event, March 10–12, 2021, Proceedings

Research Article

Interactive Domain-Specific Knowledge Graphs from Text: A Covid-19 Implementation

Download(Requires a free EAI acccount)
4 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-77417-2_18,
        author={Vin\^{\i}cius Melqu\^{\i}ades de Sousa and Vin\^{\i}cius Medina Kern},
        title={Interactive Domain-Specific Knowledge Graphs from Text: A Covid-19 Implementation},
        proceedings={Data and Information in Online Environments. Second EAI International Conference, DIONE 2021, Virtual Event, March 10--12, 2021, Proceedings},
        proceedings_a={DIONE},
        year={2021},
        month={6},
        keywords={Knowledge graphs COVID-19 Information retrieval software Natural language processing Personalized analytics},
        doi={10.1007/978-3-030-77417-2_18}
    }
    
  • Vinícius Melquíades de Sousa
    Vinícius Medina Kern
    Year: 2021
    Interactive Domain-Specific Knowledge Graphs from Text: A Covid-19 Implementation
    DIONE
    Springer
    DOI: 10.1007/978-3-030-77417-2_18
Vinícius Melquíades de Sousa, Vinícius Medina Kern

    Abstract

    Information creation runs at a higher rate than information assimilation, creating an information gap for domain specialists that usual information frameworks such as search engines are unable to bridge. Knowledge graphs have been used to summarize large amounts of textual data, therefore facilitating information retrieval, but they require programming and machine learning skills not usually available to domains specialists. To bridge this gap, this work proposes a framework, KG4All (Knowledge Graphs for All), to allow for domain specialists to build and interact with a knowledge graph created from their own chosen corpus. In order to build the knowledge graph, a transition-based system model is used to extract and link medical entities, with tokens represented as embeddings from the prefix, suffix, shape and lemmatized features of individual words. We used abstracts from the COVID-19 Open Research Dataset Challenge (CORD-19) as corpus to test the framework. The results include an online prototype and correspondent source code. Preliminary results show that it is possible to automate the extraction of entity relations from medical text and to build an interactive user knowledge graph without programming background.

    Keywords
    Knowledge graphs COVID-19 Information retrieval software Natural language processing Personalized analytics
    Published
    2021-06-15
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-030-77417-2_18
    Copyright © 2021–2025 ICST
    EBSCOProQuestDBLPDOAJPortico
    EAI Logo

    About EAI

    • Who We Are
    • Leadership
    • Research Areas
    • Partners
    • Media Center

    Community

    • Membership
    • Conference
    • Recognition
    • Sponsor Us

    Publish with EAI

    • Publishing
    • Journals
    • Proceedings
    • Books
    • EUDL