Unstructured Topic Map-Like Data Powering AI

Artificial Intelligence Is Almost Ready for Business by Brad Power.

From the post:

Such mining of digitized information has become more effective and powerful as more info is “tagged” and as analytics engines have gotten smarter. As Dario Gil, Director of Symbiotic Cognitive Systems at IBM Research, told me:

“Data is increasingly tagged and categorized on the Web – as people upload and use data they are also contributing to annotation through their comments and digital footprints. This annotated data is greatly facilitating the training of machine learning algorithms without demanding that the machine-learning experts manually catalogue and index the world. Thanks to computers with massive parallelism, we can use the equivalent of crowdsourcing to learn which algorithms create better answers. For example, when IBM’s Watson computer played ‘Jeopardy!,’ the system used hundreds of scoring engines, and all the hypotheses were fed through the different engines and scored in parallel. It then weighted the algorithms that did a better job to provide a final answer with precision and confidence.”

Granting that the tagging and annotation is unstructured, unlike a topic map, but it is as unconstrained by first order logic and other crippling features of RDF and OWL. Out of that mass of annotations, algorithms can construct useful answers.

Imagine what non-experts (Stanford logic refugees need not apply) could author about your domain, to be fed into an AI algorithm. That would take more effort than relying upon users chancing upon subjects of interest but it would also give you greater precision in the results.

Perhaps, just perhaps, one of the errors in the early topic maps days was the insistence on high editorial quality at the outset, as opposed to allowing editorial quality to emerge out of data.

As an editor I’m far more in favor of the former than the latter but seeing the latter work, makes me doubt that stringent editorial control is the only path to an acceptable degree of editorial quality.

What would a rough-cut topic map authoring interface look like?


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