The standard for defining ontologies these days is OWL and Protege. Unfortunately, OWL lacks any notion of exceptions in inheritance or any other notion of defeasibility.
So, although you may want to say that birds fly, you’re ontology will be broken (or become much more complicated) when you realize there are birds that can’t fly, such as penguins or ostriches, or even sick or injured birds.
Practically speaking, you need something like courteous logic or the defeasibility in SILK to handle this (or any 1980s expert system shell or even earlier frame system). OWL is very hard on mortal man (e.g., mainstream IT) in this regard.
How can I tell OWL that a pronoun is a noun but that pronouns are a closed class of words, unlike nouns, verbs, adjectives, and adverbs (in general). Well, I’ll have to tell it about open-class nouns versus closed class nouns. What a pain!
This is why we use Protege primarily as a drafting tool and, for example, SILK, to do reasoning. Non-defeasible description logic and first-order reasoners are difficult to get along with, in practice (and make sustainable knowledge repositories too difficult – which inhibits adoption, obviously).
In a recent post I mentioned comments by Sir Tim Berners-Lee concerning the overlap between enterprise information models and semantic web ontology supporting the concept of linked data. Sir Berners-Lee argued that overlap is already sufficient to have a transformative effect on mainstream IT. I think he is right, but also that we are not there yet. There are many obstacles to adoption, not the least of which is the inertia of enterprise IT. Disruptive approaches to software development typically require ten years or so to cross the chasm from visionary and early adopters to the mainstream. We are only a few years into this and the technology is not ready.
First, let’s establish that there is plenty of semantics available for reuse now. There are existing models, some of which are well-designed, mature, and widely used. Unfortunately, most of what exists has little apparent relevance to enterprises. There is little on this diagram that would draw the attention of an enterprise architect, for example.
Continue reading “Extended Enterprise Ontology”
I have previously written about the lack of a common upper ontology in the semantic web and commercial software markets (e.g., business rules). For example, the lack of understanding of time limits the intelligence and ease of use of software in business process management (BPM) and complex event processing (CEP). The lack of understanding of money limits the intelligence and utility of business rules management systems (BRMS) in financial services and the capital markets. And, more fundamentally, understanding time and money (among other things, such as location, which includes distance) requires a core understanding of amounts.
The core principle here is that software needs to have a common core of understanding that makes sense to most people and across almost every application. These are the concepts of Pareto’s 80/20 Principle. A concept like building could easily be out, but concepts like money and time (and whatever it takes to really understand money and time) are in. Location, including distance, is in. Luminousity could be out, but probably not if color is in. Charge and current could be out, but not if electricity or magnetism is in. The cutoff is less scientific than practical, but what is in has to be deeply consistent and completely rational (i.e., logically rigorous). Continue reading “A Common Upper Ontology for Advanced Placement tests”