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.