Deep Narrative Analysis’ (DNA’s) long-term goals will be achieved by research and experimentation, taking small steps and refining our designs. Our ultimate goal is to create a tool that compares narratives and news articles, and indicates where they align and diverge, what events are mentioned (or omitted), and what words are used. This, then, can be used to understand if a news article is biased, or if there are certain themes that are consistent across a set of narratives and articles.
To achieve this long-term goal, we need to transform the text of a news article or narrative into a semantically-rich, machine-processable format. Our choice for that format is a knowledge graph. The transformation is accomplished using syntactic and semantic processing to incrementally parse the text and then output the results as an RDF encoding (examples are included in this post).