IBM's Watson and Nuance's Dragon Medical

Earlier this week, after the Watson computer defeated human champions in a three-day match on the popular TV show Jeopardy, its creators and a Massachusetts software company prepared to sell a medical version of the smart machine.

IBM has an agreement with Nuance Communications Inc. of Burlington to sell Watson-based products to health care providers. Nuance already offers voice-recognition software for a variety of applications. The companies are pitching forthcoming technology that will, among other things, allow medical providers to describe symptoms orally and get diagnostic information in return.

Watson technology will enable Nuance to add artificial intelligence to its offerings. And that's where this blog comes in. I've been talking about these two topics, albeit separately, for some time now.

Watson's ability to analyze the meaning and context of human language, and quickly process information to find precise answers can assist decision makers, such as physicians and nurses, unlock important knowledge and facts buried within huge volumes of information, and offer answers they may not have considered to help validate their own ideas or hypotheses.

For example, a doctor considering a patient’s diagnosis could use Watson’s analytics technology, in conjunction with Nuance’s voice and clinical language understanding solutions, to rapidly consider all the related texts, reference materials, prior cases, and latest knowledge in journals and medical literature to gain evidence from many more potential sources than previously possible, thereby helping the medical professional to confidently determine the most likely diagnosis and treatment options.

For more information about the Watson computing system and the Jeopardy! challenge, click here.

On earlier posts to this blog, I discussed the semantic Web, ontologies, and speech recognition software from Nuance (and Adobe):

See, for example, my November 7, 2009 post "Vagueness, Logic and Ontology: Fuzzy Ontologies"

In traditional ontology theory, concepts and roles are crisp sets. However, there is a great deal of fuzziness in the real world.

For example, one may be interested in finding “a very strong flavored red wine” or in reasoning with concepts such as “a cold place”, “an expensive item”, “a fast motorcycle”, etc.

A possible solution to handling uncertain data is to incorporate fuzzy logic into ontologies. Unfortunately, these fuzzy ontologies have shortcomings – reasoners for fuzzy ontologies are not yet so polished as those for crisp (aka traditional) ontologies.

See, for example, my May 30, 2009 post on "Speech Recognition Software"

The trivial example of one-to-one translation given there can be extended to one-to-many translation: That is, in a matter of seconds, you could “program” Dragon Medical to type out a whole sentence in response to your speaking just a single word or code into the microphone. And, vice versa: you could “program” Dragon Medical to type out just a single word or code in response to your speaking a whole sentence into the microphone.

Mapping Ontologies - Tools, a Suite, and an Application

Before continuing, I want to devote a little space to fleshing out the subject of Mapping Ontologies, which I have alluded to in a couple of earlier posts. Mapping is a process in which we first try to find similarity between individual elements of two ontologies. We compare the elements on the basis of their names and attributes.

Using Protégé

Note: Protégé (see my August 24 post below) is probably the most popular ontology editor available.

Click here.

Using NeOn Toolkit

Note: The Watson plug-in to the NeOn Toolkit (see my September 27 post below) allows the user to select entities of the currently edited ontology and to automatically trigger queries to a remote ontology.

Click here.

A proposed Web app that addresses a real-world situation with the help of ontology mapping, probabilities, and Jena - a Semantic Web framework for Java.

When incorporating data semantics into the searching process, the correctness of searching can depend directly on mapping results.

Keywords: Protégé, OWL, Jena, Probability

Click here.

A Comprehensive Suite of Tools

A presentation by the former lead developer of Protégé-OWL

Click here.

"Bernstein at Harvard" -- Segue to Probability, Semantic Amiguity, etc.

My prior post was about Watson, a tool for accessing multiple and usually heterogeneous online ontologies, but never once mentioned the word "probability." However, the recognition of uncertainty in the real world is sometimes needed within description logics, the underpinning of ontologies like OWL. Because of its importance, I'll take up the topic of uncertain domain knowledge in my next post.

For the present, however, I've embed here the 4 1/2 minute video "Bernstein at Harvard," which includes many of the terms - e.g., probability, meaning, semantic ambiguity, meta, language, and knowledge - that I'll use in my upcoming rant. Hope you find this segue as relevant as I do.





Note: Semantic ambiguity arises when a word or concept has an inherently diffuse meaning based on widespread or informal usage. This is often the case, for example, with idiomatic expressions whose definitions are rarely or never well-defined, and are presented in the context of a larger argument that invites a conclusion.

For example, “You could do with a new automobile. How about a test drive?” The clause “You could do with” presents a statement with such wide possible interpretation as to be essentially meaningless. Lexical ambiguity is contrasted with semantic ambiguity. The former represents a choice between a finite number of known and meaningful context-dependent interpretations. The latter represents a choice between any number of possible interpretations, none of which may have a standard agreed-upon meaning. This form of ambiguity is closely related to vagueness.