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/tripl...

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  No one company can possibly do all these things, but that is no longer a requirem...