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Is business logic too much for classical logic?

Business logic is not limited to mathematical logic, as in first-order predicate calculus.

Business logic commonly requires “aggregation” over sets of things, like summing the value of claims against a property to subtract it from the value of that property in order to determine the equity of the owner of that property.

  • The equity of the owner of a property in the property is the excess of the value of the property over the value of claims against it.

There are various ways of describing such extended forms of classical logic.  The most relevant to most enterprises is the relational algebra perspective, which is the base for relational databases and SQL.  Another is the notion of generalized quantifiers.

In either case, it is a practical matter to be able to capture such logic in a rigorous manner.  The example below shows how that can be accomplished using English, producing the following axiom in extended logic:

  • ∀(?x15)(property(?x15)→∀(?x10)(owner(of)(?x10,?x15)→∃(?x31)(value(of(?x15))(?x31)∧∑(?x44)(∀(?x49)(claim(against(?x15))(?x49)→value(of(?x49))(?x44))→∃(?x26)(excess(of(?x31))(over(?x44))(?x26)∧equity(in(?x15))(of(?x10))(?x26))))))

This logic can be realized in various ways, depending on the deployment platform, such as: Continue reading “Is business logic too much for classical logic?”

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Translating English into Logic using the Linguist

Now that the patent filings are done, we can discuss and show more about the Linguist…

The following link is a video that shows a sentence from Project Sherlock being translated from English into first-order logic using the patent-pending  Linguisttm software.

The hydrophobic ends of the lipids of a cell’s plasma membrane are oriented away from the cell’s cytoplasm.

This video was recorded in October, 2012.  More recent versions of the Linguist can render the logic in more ways, such as shown below:

A grammatically disambiguated and logically formalized English sentence using Automata Linguist

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Background for our Semantic Technology 2013 presentation

In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment.  This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education.  Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration of the electronic textbook discussed below.  There is a video of that presentation here.  The introductory remarks are interesting but not pertinent to this article.

Background on Vulcan’s Project Halo

Background on Vulcan's Project Halo

From 2002 to 2004, Vulcan developed a Halo Pilot that could correctly answer between 30% and 50% of the questions on advanced placement (AP) tests in chemistry.  The approaches relied on sophisticated approaches to formal knowledge representation and expert knowledge engineering.  Of three teams, Cycorp fared the worst and SRI fared the best in this competition.  SRI’s system performed at the level of scoring a 3 on the AP, which corresponds to earning course credit at many universities.  The consensus view at that time was that achieving a score of 4 on the AP was feasible with limited additional effort.  However, the cost per page for this level of performance was roughly $10,000, which needed to be reduced significantly before Vulcan’s objective of a Digital Aristotle could be considered viable.

Continue reading “Background for our Semantic Technology 2013 presentation”

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SBVR in OWL

In preparation for generating RIF and SBVR from the Linguist, we have produced an OWL ontology for the pertinent aspects of the SBVR specification.  We hope that this is helpful to others and would sincerely appreciate any corrections or comments on how to improve it.

Paul

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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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Logic from the English of Science, Government, and Business

Our software is translating even long and complicated sentences from regulations to textbooks into formal logic (i.e,, not necessarily first-order logic, but more general predicate calculus).   As you can see below, we can translate this understanding into various logical formalisms including defeasible first-order logic, which we are applying in Vulcan’s Project Halo.  This includes classical first-order logic and related standards such as RIF or SBVR, as well as building or extending an ontology or description logic (e.g., OWL-DL).

We’re excited about these capabilities in various applications, such as in advancing science and education at Vulcan and formally understanding, analyzing and automating policy and regulations in enterprises.

English translated into predicate calculus

English translated into predicate calculus

a sentence understood by Automata

an unambiguously, formally understood sentence


English translated into SILK and Prolog

English translated into SILK and Prolog

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Recruiting: IBM Ilog vs. JBoss Drools

I received notice of a Victorian government position offering $106k, as follows, today:

BRMS Developer (WebSphere ILOG JRules)

You will have proven experience as a BRMS Developer within a Java/JEE environment using IBM‘s WebSphere ILOG JRules platform. You will have implementation experience using integration technologies (e.g. Web Services, JMS) and have the ability to liaise with and engage key stakeholders.  Ideally you will also have knowledge and/or exposure to IBM‘s WebSphere integration suite (including the MQ Series).

This got a reaction out of me since we’re looking for people (although emphasizing logic, semantics, and English rather than any particular engine).  At first, I thought it must be a Java job, but stakeholder engagement indicates this is a full-fledged knowledge engineering position.

$100k for anyone with strong, specific experience seems low.  For someone that can understand objectives and translate requirements into operational business logic, it seems lower.

I’m surprised there isn’t more of an Ilog premium, too.  JBoss Drools consultants can make more than this.

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Blaze down in Fair Isaac’s Q1 2012

FICO reported 9% growth in revenues year over year.

  • the bulk of revenues and all the growth was in pre-configured Decision Management applications
  • FICO score revenues were half as much, w/ B2B growing as B2C (myFICO) waned
  • tools revenues were less than half again as much and flat
    • optimization (XPress) was up
    • Blaze Advisor was down

This is in sharp contrast to the success that Ilog has enjoyed under the IBM umbrella.

Blaze Advisor doesn’t seem to make sense as a stand-alone tool any more.   Applications are great, and so are combinations of BI/optimization/rules, but if the BRMS tool will survive independently it needs to find more traction, perhaps outside of Fair Isaac.