Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

May 3, 2011

The Human – Computer Chasm & Topic Maps

Filed under: Authoring Topic Maps,Subject Identity,Topic Maps — Patrick Durusau @ 1:33 pm

Someone asked the other day why I thought adoption of topic maps hasn’t “set the woods on fire,” as my parents generation would say.

I am in the middle of composing a longer response with suggestions for marketing strategies but I wanted to stop and share something about the human – computer chasm that is relevant to topic maps.

Over the years the topic map community has debated various syntaxes, models, data model, recursive subject representation, query languages and the like. All of which have been useful and sometimes productive debates.

But in those debates, we sometimes (always?) over-looked the human – computer chasm when talking about subject identity.

Take a simple example:

When I see my mother-in-law I don’t think:

  1. http://www.durusau.net/general/Ethel-Holt.html
  2. Wife of Fred Holt
  3. Mother of Carol Holt (my wife)
  4. Mother-in-law of Patrick Durusau
  5. etc….

I know all those things but they aren’t how I recognize Ethel Holt.

I have known Ethel for more than thirty (30) years and have been her primary care-giver for the last decade or so.

To be honest, I don’t know how I recognize Ethel but suspect it is a collage of factors both explicit and implicit.

But topic maps don’t record our recognition of subjects. They record our after the fact explanations of how we think we recognized subjects. To be matched with circumstances that would lead to the same explanation.

I think part of the lack of progress with topic maps is that we failed to recognize the gap between how we recognize subjects and what we write down so computers can detect when two statements are about the same subject.

What topic maps are mapping, isn’t between properties of subjects (although it can be expressed that way) but between the reasons given by some person for identifying a subject.

The act of recognition is human, complex and never fully explained.

Detecting subject sameness is mechanical and based on recorded explanations.

That distinction makes it clear the choices of properties, syntax, etc., for subject sameness, are a matter of convenience, nothing more.

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