The Data Day Texas 2020 Sessions

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Automated Encoding of Knowledge from Unstructured Natural Language Text into a Graph Database

Chris Davis - Lymba

Most contemporary data analysts are familiar with mapping knowledge onto tabular data models like spreadsheets or relational databases. However, these models are sometimes too broad to capture subtle relationships between granular concepts among different records. Graph databases provide this conceptual granularity, but they typically require that knowledge is curated and formatted by subject matter experts, which is extremely time- and labor-intensive. This presentation presents an approach to automate the conversion of natural language text into a structured RDF graph database.