It frequently happens that a group of experts use a term so differently that they just cannot agree on a single meaning or definition. This problem arises in spades in the area of ‘risk’. For example, in traditional operational risk management (ORM), when you measure risk, you multiply the probability of a loss times the amount of the loss. In the modern view of ORM, risk is a measure of loss at a level of uncertainty. The modern definition of risk requires both exposure and uncertainty[1]. So you get two different numbers if you measure risk from these different perspectives. One can go round and round with a group of experts trying to agree on a definition of ‘risk’ and generate a lot of heat with little illumination. But, when we change our perspective from the term, and instead start looking for underlying concepts that everyone agrees on we don’t have to look very far. When we found them, we expressed them in simple non-technical terms to minimize ambiguity. Here they are:
- Something bad might happen
- There is a likelihood of the bad thing happening
- There are undesirable impacts whose nature and severity varies (e.g. financial, reputational)
- There is a need to take steps to reduce the likelihood of the bad thing happening, or to reduce the impact if it does happen.
After many discussions and no agreement on a definition the term, ‘risk’, we wrote down these four things and asked the experts: “when you are talking about risk, are you always talking about some combination of these four things”? “Yes” was unanimous. The experts differ on how to combine them and what to call them. For example, the modern view and the traditional view of risk each combine these underlying concepts in different ways to define what they mean by ‘risk’. In the modern view, if the probably of loss is 100%, there is no risk because there is no uncertainty. The concept that is called ‘risk’ in the traditional view, is called ‘expected loss’ in the modern view, but it is the same underlying concept. Compared to wading through the muck and the mire of trying to agree on terms, focusing on the underlying concepts using simple non-jargon terms is like a hot knife going through cold butter. Terms get in the way of a happy marriage too! How many times have you disagreed with your partner on the meaning of a word? It’s more than just semantics, it’s often emotional too. I believe we are all divided by a common language, in that no two people use words to mean exactly the same thing, even everyday words like “support” or “meeting”. I have learned that it is easier to learn and use the language of my spouse than it is to convince her that the term I use is the right one (despite the seductive appeal of the latter).
For further reading, refer to Michael Uschold’s additional posts in this series.
Last week I was staring at the concept “unemployment rate” as part of a client project. Under normal light (that is, using traditional modeling approaches) we’d see “unemployment rate” as a number, probably attach it to the thing that the unemployment rate was measuring, say the US Economy, and be done with it. But when we shine the semantic spectrometer at it, the constituent parts start to light up. It is a measurement. Stare at those little lines a bit harder: the measurement has a value (because the term “value” is overloaded, in gist we’d say it has a magnitude) and the magnitude is a percentage. Percentages are measured in ratios and this one (stare a little harder at the spectrograph) is the ratio of two populations (in this case, groups of humans). One population consists of those people who are not currently employed and who have been actively seeking employment over the past week, and the other is the first group plus those who are currently employed. These two populations are abstract concepts until someone decides to measure unemployment. At that time, the measurement process has us establish an intensional group (say residents of Autuaga County, Alabama) and perform some process (maybe a phone survey) of some sample (a sub population) of the residents. Each contacted resident is categorized into one of three sub sub populations (currently working, currently not working and actively seeking work, and not working and not actively seeking work). Note: there is another group that logically follows from this decomposition, is not of interest to the Bureau of Labor Standards, but is of interest to recruiters: working and actively seeking employment. Finally, the measurement process dictates whether the measure is a point in time or an average of several measures made over time. This seems like a lot of work for what started as just a simple number. But look at what we’ve done: we have a completely non-subjective definition of what the concept means. We have a first class concept that we can associate with many different reference points, for example, the same concept can be applied to National, State, or Local unemployment. An ontology will organize this concept in close proximity to other closely-related concepts. And the constituent parts of the model (the populations for instance) are now fully reusable concepts as well. The other thing of interest is that the entire definition was built out of reusable parts (Magnitude, Measurement, Population, Measurement Process, Residence, and Geographic Areas) that existed (in gist) prior to this examination. The only thing that needed to be postulated to complete this definition was what would currently be two taxonomic distinctions: working and seeking work. David, thanks for that analogy.
Here is my home email graph (that dense cloud above is the co-housing community I belong to and source of most of my home email traffic). Everybody’s name is on their nodes. All patterns of interaction are revealed. And this is just one view. There are potentially views over time, views with whether an email was read, responded to, how soon etc., etc. Imagine this data for everyone in the country, coupled with the same data for phone calls. If that doesn’t raise the hackles of your civil libertarianism, then nothing will. ‘Course, it’s only metadata. by Dave McComb
Michael Uschold, Semantic Arts Senior Ontologist, will share a six part series on the importance of distinguishing terms from concepts when building enterprise ontologies for corporate clients. This first article summarizes five key points; each is elaborated in greater detail in the subsequent posts. (Note: The titles of each post will become linkable below as they are published.) The importance of distinguishing Terms and Concepts: Overview
