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Over the last month, OntoInsights has developed a prototype application to analyze human narratives, utilizing linguistic and semantic theory, and building on open-source offerings in machine learning and natural language processing. The application shows how the concepts behind Deep Narrative Analysis (DNA, as discussed in our first blog post, The Power of Narrative) can automatically convert stories in the form of unstructured text, into machine-analyzable knowledge graphs that retain all their richness.

In this blog post, we overview the structure of the DNA application (which is available in the dna directory of our project on GitHub). You might be interested in the structure if you want to review or reuse our code to process PDFs and unstructured text, get background Wikidata, create a simple GUI, and more.

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When people listen to stories, they hear about persons, places, and things.  When people listen to great stories, they experience events, situations, outcomes and actions.  From stories, we learn about the challenges, opportunities, feelings and decisions faced by others in a way that enables us to better understand the entire experience and relate to it.

That is why at OntoInsights, we start our work focused on verbs (focused on what is happening and being experienced).  When we start with verbs, we can construct a complex timeline and add in who, what, where, when, and why.  This is a powerful approach to storytelling, and a powerful approach to analysis to gain a holistic view of the experience of the storyteller.

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Welcome to the first OntoInsights blog post which explains the company’s focus on narratives.

People have listened to and studied stories for insights into cultures, customs, values and life. They have used narratives to explain how and why the world works, and their experiences in it. Stories are humans’ approach to structuring knowledge and providing insight.

Listeners can easily extract knowledge from narratives, but for a computer to do this, a combination of approaches and technologies is needed:

  • Natural language processing (NLP) to parse the text
  • Semantic (ontological) and linguistic understanding to distill meaning
  • Graph technologies to encode the events of the narratives and background knowledge
  • Pattern recognition algorithms to discover similarities and differences
  • Inference, reasoning and causal analysis to extract knowledge and provide explanations

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