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- W4366999326 abstract "Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata and leverages scientific domain knowledge to synthesize a new dataset from the original to construct knowledge graphs. We demonstrate our approach's effectiveness through a case study on the natural hazard engineering dataset on ``LEAP Liquefaction'' hosted on DesignSafe. Traditional lexical search on DesignSafe is limited in uncovering hidden relationships within the data. Our knowledge graph enables complex queries and fosters new scientific insights by accurately identifying relevant entities and establishing their relationships within the dataset. This innovative implementation can transform the landscape of data-driven discoveries across various scientific domains." @default.
- W4366999326 created "2023-04-27" @default.
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- W4366999326 date "2023-04-21" @default.
- W4366999326 modified "2023-09-30" @default.
- W4366999326 title "Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe" @default.
- W4366999326 doi "https://doi.org/10.48550/arxiv.2304.11273" @default.
- W4366999326 hasPublicationYear "2023" @default.
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