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Simply Smarter Intelligent Agents

Deep learning can produce some impressive chatbots, but they are hardly intelligent.  In fact, they are precisely ignorant in that they do not think or know anything.

More intelligent dialog with an artificially intelligent agent involves both knowledge and thinking.  In this article, we educate an intelligent agent that reasons to answer questions.

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Nominal semantics of ‘meaning’

Just a quick note about a natural language interpretation that came up for the following sentence:

  • Under that test, the rental to an oil well driller of a “rock bit” having an effective life of but one rental is a transaction in lieu of a transfer of title within the meaning of (a) of this section.

The NLP system comes up with many hundreds of plausible parses for this sentence (mostly because it’s considering lexical and syntactic possibilities that are not semantically plausible).  Among these is “meaning” as a nominalization.

From Wikipedia:

  • In linguistics, nominalization is the use of a word which is not a noun (e.g. a verb, an adjective or an adverb) as a noun, or as the head of a noun phrase, with or without morphological transformation.

It’s quite common to use the present participle of a verb as a noun.  In this case, Google comes up with this definition for the noun ‘meaning’:

  • what is meant by a word, text, concept, or action.

The NLP system has a definition of “meaning” as a mass or count noun as well as definitions for several senses of the verb “mean”, such as these:

  1. intend to convey, indicate, or refer to (a particular thing or notion); signify.
  2. intend (something) to occur or be the case.
  3. have as a consequence or result.

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Event-centric BPM and goal-driven processing

The slides for my Business Rules Forum presentation on event semantics and focusing on events in order to simplify process definition and to facilitate more robust governance and compliance are at Event-centric BPM.

After the talk I spoke with Jan Verbeek and Gartjan Grijzen of Be Informed and reviewed their software, which is excellent.  They have been quite successful with various government agencies in applying  the event-centric methodology to produce goal-driven processing.  Their approach is elegant and effective.  It clearly demonstrates the merits of an event-centric approach and the power that emerges from understanding event-dependencies.  Also, it is very semantic, ontological, and logic-programming oriented in its approach (e.g., they use OWL and a backward-chaining inference engine).

They do not have the top-down knowledge management approach that I advocate nor do they provide the logical verification of governing policies and compliance (i.e., using theorem provers) that I mention in the talk (see Guido Governatori‘s 2010 publications and Travis Breaux‘s research at CMU, for example) but theirs is the best commercially deployed work in separating business process description from procedural implementation that comes to mind. (Note that Ed Barkmeyer of NIST reports some use of SBVR descriptions of manufacturing processes with theorem provers.  Some in automotive and aerospace industries have been interested in this approach for quality purposes, too.)

BeInformed is now expanding into the United States with the assistance of Mills Davis and others.  Their software is definitely worth consideration and, in my opinion, is more elegant and effective than the generic BPMN approach.

What is has always been going to be

I’ve been working for a while now on an ontology for representing events (which includes process, of course).  One of the requirements of a system that is to monitor, govern, implement, or reason about processes is that it consider “situations”, which are things that happen or occur, including events and states.  (See, for example, the perdurants of the DOLCE ontology, BFO‘s occurents, or OpenCyc’s situations.)  This requires the representation of time-variant information at various points or during various intervals of time (more than just the Allen relations or OWL Time).   If you’re interested in such things, I’d recommend Parsons‘ “Events in the Semantics of English” or Pustejovsky‘s “Syntax of Event Structure“, both of which look at the subject from a linguistic rather than inferential perspective.  When you pursue this to the point that you implement the axioms that an artificial intelligence needs to provide assistance in defining or governing a business process (or answering questions about molecular biological processes) you land up in some pretty abstract stuff, including the Stanford Encyclopedia of Philosophy.  I found the title of this post entertaining within the page on temporal logic.

IBM Ilog JRules for business modeling and rule authoring

If you are considering the use of any of the following business rules management systems (BRMS):

  • IBM Ilog JRules
  • Red Hat JBoss Rules
  • Fair Isaac Blaze Advisor
  • Oracle Policy Automation (i.e., Haley in Siebel, PeopleSoft, etc.)
  • Oracle Business Rules (i.e., a derivative of JESS in Fusion)

you can learn a lot by carefully examining this video on decisions using scoring in Ilog.  (The video is also worth considering with respect to Corticon since it authors and renders conditions, actions, and if-then rules within a table format.)

This article is a detailed walk through that stands completely independently of the video (I recommend skipping the first 50 seconds and watching for 3 minutes or so).  You will find detailed commentary and insights here, sometimes fairly critical but in places complimentary.  JRules is a mature and successful product.  (This is not to say to a CIO that it is an appropriate or low risk alternative, however. I would hold on that assessment pending an understanding of strategy.)

The video starts by creating a decision table using this dialog:

Note that the decision reached by the resulting table is labeled but not defined, nor is the information needed to consult the table specified.  As it turns out, this table will take an action rather than make a decision.  As we will see it will “set the score of result to a number”. As we will also see, it references an application.  Given an application, it follows references to related concepts, such as borrowers (which it errantly considers synonomous with applicants), concerning which it further pursues employment information.

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Google vs. Facebook and Bing (again)

Almost a year ago, I wrote about semantics and social networking as threats to Google.  In that post, I referenced a prior article on investments in natural language processing, such as Microsoft’s acquisition of Powerset, which is now part of Bing.

Today, there are two articles I recommend.  The first addresses the extent to which Google’s Superbowl ad is a response to the threat from Bing.  The second addresses Facebook overtaking Google.

What could be more strategic than process or decision management?

The folks from Knowledge Partners have a post that I found thanks to Sandy Kemsley, whose blog often provides good pointers.  This article talks about the decision perspective on business rules.  It makes some good points, on which I would like to elaborate albeit at a more semantic or knowledge-level.

Every language has three kinds of statements: questions, statements, and commands.   There are also some peripheral types, such as exclamations (Yikes!), but in business processes and decisions only declarative and imperative sentences matter.

From a process- or decision-oriented perspective, decisions are always phrased as imperative sentences.  Otherwise, the statements reflected in any business process, whether you are using BPMN or a BRMS, are declarative sentences.

Decisions are imperative sentences because they state an action to be taken.  For example, decline a loan or offer a discount.  It’s really pretty simple.  A decision is an action.  Rules that don’t take actions are statements of truth.  Such declarative statements of truth are perfect for formal logic, logic programming, and semantic technologies.  It’s the action that requires the production rule technology that dominates the market for and applications of rules.

The authors of the aforementioned article use the following diagram to explain the benefits of the decision-oriented approach in simplifying business processes:

The impact of rules on business process complexity

The impact is very simple.  If you eliminate how you reach decisions from the flow that you diagram in BPMN things get simpler.  It’s really as simple as realizing that you have removed all the “if” parts (i.e., the antecedents) of the rule logic from the flow chart.

So who in their right mind would use a business process tool to express any business logic? (more…)

How is a process an event?

Today, I came upon some commentary by a business rule colleague, Carlos Serranos-Morales, of Fair Isaac concerning a presentation I made at the Business Rules Forum.  During the presentation I showed some sentences that are beyond the current state of the art in the business rules industry.  Generally speaking, these were logical statements that did not use the word “if”.  (Note, however, that many of the them could be expressed in SBVR, OMG’s semantics of business vocabulary and rules standard).  Carlos argued that such statements should be more precisely articulated within the specific context of a business process. 

Here is the slide that triggered the controversy:

AI beyond Fair Isaac

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Agile decision services without XML details

Externalizing enterprise decision management using service-oriented architecture orchestrated by business process management makes increases agility and allows continuous performance improvement, but…

How do you implement the rules of EDM in an SOA decision service?  (more…)