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Deep QA

Our efforts at acquiring deep knowledge from a college biology text have enabled us to answer a number of questions that are beyond what has been previously demonstrated.

For example, we’re answering questions like:

  1. Are the passage ways provided by channel proteins hydrophilic or hydrophobic?
  2. Will a blood cell in a hypertonic environment burst?
  3. If a Paramecium swims from a hypotonic environment to an isotonic environment, will its contractile vacuole become more active?

A couple of these are at higher levels on the Bloom scale of cognitive skills than Watson can reach (which is significantly higher than search engines).

As some other posts have shown in images, we can translate completely natural sentences into formal logic.  We actually do the reasoning using Vulcan’s SILK, which has great capabilities, including defeasibility.  We can also output to RIF or SBVR, but the temporal aspects and various things such as modality and the need for defeasibility favor SILK or Cyc for the best reasoning and QA performance.

One thing in particular is worth noting:  this approach does better with causality and temporal logic than is typically considered by most controlled natural language systems, whether they are translating to a business rules engine or a logic formalism, such as first order or description logic.  The approach promises better application development and knowledge management capabilities for more of the business process management and complex event processing markets.

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Simple problems with the semantic web

The standard for defining ontologies these days is OWL and Protege.  Unfortunately, OWL lacks any notion of exceptions in inheritance or any other notion of defeasibility.

So, although you may want to say that birds fly, you’re ontology will be broken (or become much more complicated) when you realize there are birds that can’t fly, such as penguins or ostriches, or even sick or injured birds.

Practically speaking, you need something like courteous logic or the defeasibility in SILK to handle this (or any 1980s expert system shell or even earlier frame system).  OWL is very hard on mortal man (e.g., mainstream IT) in this regard.

How can I tell OWL that a pronoun is a noun but that pronouns are a closed class of words, unlike nouns, verbs, adjectives, and adverbs (in general).  Well, I’ll have to tell it about open-class nouns versus closed class nouns.  What a pain!

This is why we use Protege primarily as a drafting tool and, for example, SILK, to do reasoning.   Non-defeasible description logic and first-order reasoners are difficult to get along with, in practice (and make sustainable knowledge repositories too difficult – which inhibits adoption, obviously).