Hearing All Voices

Over the last few years, there has been an on-going, vigorous debate regarding the future of artificial intelligence (AI) and machine learning (ML), and what needs to be developed. The debate comes down to using only machine learning technologies (based on different mathematical models and performing correlation/pattern analysis) versus using a combination of machine learning and "classical AI" (i.e., rules-based and expert systems). (Note that no one believes that rules-based systems alone are enough!) You can read about those debates in numerous articles (such as in the MIT Technology Review , ZDNet's summary  of the December 2020 second debate, and Ben Dickson's TechTalks ).  Given my focus on knowledge engineering, I tend to land on the side of the "hybrid" approach (spearheaded by Gary Marcus in the debates) that combines ML and classical AI, and then I add on ontologies (to provide formal descriptions of the semantics of things and their relationships

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. Our approach to development is to start by encoding the basics of a design approach or algorithm in a Jupyter notebook, or use a notebook to generate the knowledge/triples f