[Editor's Note: This week, we welcome Yefim "Jeff" Zhuk of Sallie Mae as he presents a series on Knowledge-Driven Architecture. This series follows up the author’s presentation at the recent international 2011 Semantic Technology Conference San Francisco and further expands on the subject of integrated software and knowledge engineering, originally described by Mr. Zhuk in the book “Integration-ready Architecture and Design.” Part I | Part II | Part III]

Part IV – Creating a semantically rich service environment locally and across industry

Part III focused on the Conversational Semantic Decision Support (CSDS) and related Use Cases.

This example can be expanded from requirements to design and development phases, including hints on service names and application messages. Standards, recommendations and best practices offered by W3C [6] can serve as the base for conversational scripts, which would help a SME, (in this case, a software developer) to successfully implement them and create a truly semantically rich SOA environment.
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[Editor's Note: This week, we welcome Yefim "Jeff" Zhuk of Sallie Mae as he presents a series on Knowledge-Driven Architecture. This series follows up the author’s presentation at the recent international 2011 Semantic Technology Conference San Francisco and further expands on the subject of integrated software and knowledge engineering, originally described by Mr. Zhuk in the book “Integration-ready Architecture and Design.” Part I | Part II | Part IV]

Part III - Transitioning From “What” to “How” and explaining Conversational Semantic Decision Support (CSDS) with Use Cases

a)      Formalization of Business Rules

One of the current development trends is a shift to rule-based applications. As more flexible and quickly adaptive to business changes, rule-based applications live a longer life and provide higher return on investment.

Conversational semantic decision support can be very helpful in the process of collecting and formalizing the rules [5]. CSDS will make sure that the rules are expressed in the known terms and the rules criteria are directly tied to existing data.
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[Editor's Note: This week, we welcome Yefim "Jeff" Zhuk of Sallie Mae as he presents a series on Knowledge-Driven Architecture. This series follows up the author’s presentation at the recent international 2011 Semantic Technology Conference San Francisco and further expands on the subject of integrated software and knowledge engineering, originally described by Mr. Zhuk in the book “Integration-ready Architecture and Design.” Part I | Part III | Part IV]

Part II

Looking for a black cat in a dark room

In the corporate world, each clerk and department has their own knowledge compartment.

Prepared for consumption by an author or a single group, information is based on “tribal knowledge” assumptions and naturally has multiple gaps, especially for other groups and departments. In increasingly interconnected businesses, informational gaps lead to productivity loss.

Compartmentalized information is usually hidden and locked inside complex tools. No surprise that we spend from 30 to 50% time looking for information. Not because we love searching… It’s just hard to find something that was hidden (not intentionally!) and especially something that has never been captured.

We often find ourselves looking for a black cat in a dark room.
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[Editor's Note: This week, we welcome Yefim "Jeff" Zhuk of Sallie Mae as he presents a series on Knowledge-Driven Architecture. This series follows up the author’s presentation at the recent international 2011 Semantic Technology Conference San Francisco and further expands on the subject of integrated software and knowledge engineering, originally described by Mr. Zhuk in the book “Integration-ready Architecture and Design.”]

Business and technical people don’t always understand each other. (That might be an understatement.)

While technology speaks XML and Web Services, business prefers natural language.

Translation from business to technology is called the development process.

“Cooking” an application involves several translation layers and teams:



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