Tuesday, January 17, 2012

Taxonomy Evolution Conundrum

Our team has been developing our taxonomy for almost ten years now. Our goal is to classify businesses by looking at how they operate, who they serve, and what they do, and our focus has been on media and software businesses. Needless to say over the last ten years, there have been major changes to the media and software industries with the introduction of smart phones, tablet computers, cloud computing, SaaS, virtualization, etc. To handle this evolution of the content we are classifying, we need to make sure our framework was solid and that the taxonomy could change with abilities to add nodes, merge nodes, link nodes, and to make sure our classifications migrated with the changes. However, change is never apparent when it happens. When we saw the first business operating in Social Networking, we originally had them classified basically as forums of user generated content, as opposed to editorial content. But as the business and technology took off, and showed itself to be a new business model, we realized we had to add the term Social Networking to our taxonomy. Now our problem was that we had to go back and re-evaluate our companies that were classified as forums and see if they were really Social Networks. One way to fix this problem is to have an auto-classifier, and you set up a new set of rules to recognize Social Networking. Then  you re-run the auto-classifier on those companies. But here is the conundrum, we noticed this evolution in business models because we had human eyes seeing the trend. How can you expect an auto-classifier to see that? What are your thoughts on this problem?

2 comments:

  1. My thought is that autoclassifiers can recognize new statistical evaluation of proximity phenomena of known terms with new linkages or new terms altogether. The machine can't "know" what humans have in mind when they write these new things. The only way that can be effective is with humans in the loop disambiguating the meaning. The ontologists and taxonomists. Of course that is the librarian in me speaking!

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  2. Good thought on using statistics to find large groupings of data and to see if it could be sub-classed better. Thanks.

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