I’ll also give you two examples of using ontologies — as in “collections of things and relationships between things” — for real-world tasks that are much dumber than AI.
ABBYY attempted to create a giant ontology of all concepts, then develop parsers from natural languages into “meaning trees” and renderers from meaning trees into natural languages. The project was called “Compreno”. If it worked, it would’ve given them a “perfect” translating tool from any supported language into any supported language without having to handle each language pair separately. To my knowledge, they kept trying for 20+ years and it probably died because I google Compreno every once in a few years and there’s still nothing.
Let’s say you are Nestle and you want to sell cereal in 100 countries. You also want to be able to say “organic” on your packaging. For each country, you need to determine if your cereal would be considered “organic”. This also means that you need to know for all of your cereal’s ingredients whether they are “organic” by each country’s definition (and possibly for sub-ingredients, etc). And there are 50 other things that you also have to know about your ingredients — because of food safety regulations, etc. I don’t have first-hand knowledge of this, but I was once approached by a client who wanted to develop tools to help Nestle-like companies solve such problems; and they told me that right now their tool of choice was custom-built ontologies in Protege, with relationships like is-a, instance-of, etc.
I’ll also give you two examples of using ontologies — as in “collections of things and relationships between things” — for real-world tasks that are much dumber than AI.
ABBYY attempted to create a giant ontology of all concepts, then develop parsers from natural languages into “meaning trees” and renderers from meaning trees into natural languages. The project was called “Compreno”. If it worked, it would’ve given them a “perfect” translating tool from any supported language into any supported language without having to handle each language pair separately. To my knowledge, they kept trying for 20+ years and it probably died because I google Compreno every once in a few years and there’s still nothing.
Let’s say you are Nestle and you want to sell cereal in 100 countries. You also want to be able to say “organic” on your packaging. For each country, you need to determine if your cereal would be considered “organic”. This also means that you need to know for all of your cereal’s ingredients whether they are “organic” by each country’s definition (and possibly for sub-ingredients, etc). And there are 50 other things that you also have to know about your ingredients — because of food safety regulations, etc. I don’t have first-hand knowledge of this, but I was once approached by a client who wanted to develop tools to help Nestle-like companies solve such problems; and they told me that right now their tool of choice was custom-built ontologies in Protege, with relationships like is-a, instance-of, etc.