Technology

 

Technology Overview

See the Coherent technology in action in video demo presentations.

Our technology manages and reasons with logical knowledge that can be authored in unrestricted English.   It includes most centrally:

  • A new method of reasoning with rich semantic rules.  The rules are highly expressive yet scalable.  The rules extend, and work well together with, databases and ontologies.  The form of rules and reasoning is based on recent theoretical advances in logical knowledge representation.
  • A new method for rapid authoring of rich semantic rules starting from English.  The meaning of an English sentence is captured deeply and precisely.  What can be stated in the English is effectively nearly unrestricted.

The capabilities also include to:

  •  Translate, assimilate, synthesize, transform, articulate, share,  explain, and debug knowledge

Developers appreciate that one can author rich RIF knowledge starting from text, combine it with knowledge in the form of OWL/RDF and SPARQL/SQL databases, and then execute reasoning with all this knowledge in XSB, an ISO Prolog system that is open source.

Benefits:

The core technology has several unique qualitative (not just incremental) advantages in comparison to competing approaches (rule-based and text-based systems).  Key benefits include:

  • More rapid authoring of knowledge
    • Reduces cost
  • Less onerous restrictions on the English when authoring knowledge
  • Expressively richer knowledge
    • In technical speak:  defeasible higher-order logic formulas, and several kinds of meta info including provenance and prioritization
  • Less effort on ontology modeling and mapping
    • Ontology emerges naturally from English phrases
  • Less brittle in face of change and inconsistency-type conflict
    • Permits exceptions — indeed, embraces and manages them
    • Maintains consistency of conclusions
    • Gracefully represents change in the world and change in knowledge
    • Handles conflict that inevitably arises in cross-organizational knowledge integration from errors, confusions, and tacit context
  • More scalable computationally
    • Leverages fully semantic bounded rationality techniques that provide valves on reasoning but also predictable behavior
  •  Stronger knowledge interchange of (logical) knowledge via standardized semantics and semantic web
    • Interoperates with all major existing semantic web standards for knowledge and reasoning, including from W3C, RuleML, and ISO
    • Enables flexible choice of deployment platform for run-time reasoning, avoiding customer lock-in
    • More completely translates knowledge between standards
  •  More scalable socially
    • Combine and evolve knowledge more smoothly, from a wide set of authors and organizations
  •  More detailed and informative explanations
    • including ‘why-not’
  •  More accurate answers and conclusions