Hearing All Voices

A previous blog post  discussed the design of the DNA Ontology and its focus on events and situations (e.g., verbs). The ontology is designed to be general and flexible. So, the OntoInsights team sought to evaluate its usability in a totally different domain ... to capture and fuse Environment, Social and Governance (ESG) data. To this end, we created and hosted a sample knowledge graph as part of the  Hanken Quantum Hackathon 2021 . The graph fused information about 1900+ companies -- including their industries, profits, environmental impacts, and country of headquarters -- and combined that with data about their "headquarters" country.  This post explores our experiences in creating that knowledge graph. The company data provided dollar amounts (in US Dollars) for various types of environmental impacts. This data was extracted (using Python code) from a spreadsheet based on the Harvard study, Corporate Environmental Impact: Measurement, Data and Information . The

Deep Narrative Analysis (DNA) makes extensive use of ontology and knowledge graph technologies. Unfortunately, these topics are not well understood. In fact, there are entire books dedicated to these subjects, as well as multiple 10+ page papers. blog posts and web sites. But those definitions can be complicated and not very meaningful to IT and business people. This post is an attempt to provide simple definitions. An ontology can be specified as: A description of the kinds of things and relationships in a topic area, specified in a formal way, and created by a community of users for an explicit purpose Unpacking the definition, it is important to highlight that: One of the most important goals of an ontology is to communicate the concepts and knowledge (and increase the understanding) of the topic area within the "community of users" This enables sharing and reuse of the knowledge encoded using the ontology The "description" requires understanding and detailing th

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

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. This blog post introduces a new starting point for ontology development that is novel in that it focuses on verbs. The approach begins by understanding the events and situations of interest in the domain being described, versus trying to catalog all the possible &

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 requirement. The current software development e