What Is I.B.M.’s Watson? appears in the New York Time Magazine on 20 June 2010. IBM or more precisely David Ferrucci and his team at IBM have made serious progress towards a useful question-answering machine. (On Ferrucci see, Ferrucci – DBLP, Ferrucci – Scientific Commons)
It won’t spoil the article to say that raw computing horsepower (BlueGene servers) plays a role in the success of the Watson project. But, there is another aspect of the project that makes it relevant to topic maps.
Rather than relying on a few algorithms to analyze questions, Watson uses more than a hundred and as summarized by the article:
Another set of algorithms ranks these answers according to plausibility; for example, if dozens of algorithms working in different directions all arrive at the same answer, it’s more likely to be the right one. In essence, Watson thinks in probabilities. It produces not one single “right” answer, but an enormous number of possibilities, then ranks them by assessing how likely each one is to answer the question.
Transpose that into a topic maps setting and imagine that you are using probabilistic merging algorithms that are applied interactively by a user in real time.
Suddenly we are not talking about a technology for hand curated information resources but an assistive technology that would enable human users go deep knowledge diving into the sea of information resources. While generating buoys and markers for others to follow.
Our ability to do that will depend on processing power, creative use and development of “probabilistic merging” algorithms and a Topic Maps Query Language that supports querying of non-topic map data and creation of content based on the results of those queries.
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PS: For more information on the Watson project, see: What Is Watson?, part of IBM’s DeepQA project.