By Gordon Rugg
This is a brief overview of an invaluable concept. It links closely to graph theory, which we describe in another recent article, and to laddering, which we will describe in a later article. All three of these concepts are powerful, simple and elegant, and all three are not as widely known as they should be.
Facet theory in the strict sense is a concept from librarianship and information science. The core concept is that you use several different ways of categorising the things that you’re categorising. This lets you organise the same set of entities in different ways for different purposes.
For instance, you could categorise vehicles either in terms of the facet place of origin, or in terms of the facet cost. Here’s what such a pair of categorisations might look like.
Place of origin facet Cost facet
In terms of place of origin, the car represented by the green node is closest to the one represented by the yellow node. In terms of cost, however, it is closest to the car represented by the red node. (We’ve kept the higher-level nodes the same shade of blue, to reduce visual distraction.)
Separating out the facets in this way makes it much easier to see the different categorisations that are being used, and makes it much easier to understand what is going on. With this representation, the separate facets are clearly distinguishable.
That’s very different from what you’d get if you tried using a statistical cluster algorithm, which would try to cram everything into a single measure of similarity. Sometimes, using a separate illustration for each facet makes the situation much clearer than if you try to crush everything into a single statistical measure or a single diagram.
This concept is similar to the concept of viewpoints, but not identical to it. Usually a viewpoint corresponds to a role occupied by a human being, so the same person can have more than one role simultaneously (e.g. the role of teacher and the role of parent). All viewpoints can be treated as facets, but not all facets are the same as viewpoints. For instance, a person evaluating teaching materials from the viewpoint of teacher might use multiple different facets for evaluating those materials, such as cost and durability, all from within the same viewpoint.
If you’d like to find out more about classic facet theory, there’s an extensive literature on it within librarianship and information science, where it’s usually described as faceted classification systems. The most famous early work in this area was by Ranganathan, later publicised by Vickery.
If you’d like to find out more about formal computational approaches to the same concept, then the literature on knowledge representation within Artificial Intelligence is a good place to start. That literature includes sophisticated discussions of the taxonomic issues.