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