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    Generating Knowledge Graphs using GPT-J for Automated Story Generation Purposes

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    DANI-UNDERGRADUATERESEARCHOPTIONTHESIS-2022.pdf (500.4Kb)
    Date
    2022-05
    Author
    Dani, Samihan Ashay
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    Abstract
    Automated story generation is a recent challenge in artificial intelligence that aims to create stories in coherent ways. While it’s made much progress, stories are still not coherent; however, a possible solution is to create a knowledge graph, or map of the world, to guide a story. This paper aims to create a knowledge graph using a large pre-trained model called GPT-J. GPT-J was prompted based on a type of noun, and responses were then filtered and shortened to get a suitable answer to be added to the knowledge graph. GPT-J showed to contain many words that were not included in another commonly used knowledge graph called ConceptNet5. Most notably, nouns referring to people had a far lower amount of related words in ConceptNet5 compared to those produced by this system.However, there are still serious limitations of this system since human respondents only responded saying 41% of responses were good responses; thus major improvements are needed.
    URI
    http://hdl.handle.net/1853/66753
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