Software Semantic Evolution, Part 6

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Click to learn more about Yefim (Jeff) Zhuk. Catch up on this blog series with Part 1Part 2Part 3Part 4, and Part 5.

SOA and Microservices, RAML and DataSense by MuleSoft, and the Next Step

Collaboration of Services and Transformation of “tribal knowledge”

Collaboration between people and groups seems to be a thing with a positive sign, although we know how difficult this can be. Distributed knowledge and process systems [9] “allows involved parties, people and companies, negotiate multiple forms of collaboration online while sharing data and services.”

What is the need for collaboration for services?

Collaborative security of service groups is different from a single service security.

Simultaneous activity of many services, working on a common task, requires collaborative decision making. Think of a situation with multiple transportation services on the ground and in the air, when their interaction and collaboration is the must.


How can computer services optimize their behavior, when many of them simultaneosly perform different and sometimes conflicting tasks, interfere with external events and weather, trying to adapt to a quickly changing situation?

Collaborative Security and Decision Making in SOA environment [10], answers this question and turns this beautiful idea into a working system.

One of the keys, is a multi-dimensional system of rules driving service behaviour. Another key, similar to people’s collaboration, is the ability of system services to converse, understand, and adapt to the changes by adding or updating the rules. The difficult part is the mixture of business and technical slangs in expressing events and situations.

Generally speaking, business prefers natural language, while technical language is XML and web services standards. Necessity of the semantic bridge is obvious. The bridge is coming especially handy when Subject Matter Experts must intervene in an unexpected situational scenario.

What is the source of rules and how to establish correct rules for a selected rules engine?

Current practice answer this question by calling consultants. This is not only expensive. The biggest problem is that consultants do not know specifics of the business, the knowledge domain that is essential for creating the rules.

Some knowledge can be retrieved via published resources, corporate regulations and policies. But the research shows that about 70% of information is so called “tribal knowledge”, never computerized experience of subject matter experts.

The Rules Collector system [11] helps capturing the expertise of an individual in a formalized manner as a set of rules for a selected rules engine. The transformation happens over a long process initiated by a program to retrieve a complete information from a subject matter expert, sufficient enough to be formalized as a rule.

Yes, a computer conducts an interrogation of a Subject Matter Expert (SME), clarifying ambiguous expressions and connecting the dots, word by word.

At this time of massive retirement of the “baby boomers” in various industries, capturing their “tribal” knowledge becomes one of our most important tasks.

Capturing corporate knowledge in a computerized form is a pre-requisite for the next step in the development process, when the “know how” will belong to the computers.

Less technical translations and translators will be needed, and many more developers will come up with creative ideas for this exciting development stage.

For a long time, artificial intelligence (AI) lived on the bottom of the lake of opportunities. Recent years turned the lake into the ocean and the underwater current brought AI back to the surface.

Nothing else is growing so quickly with the demand for new skills and talents.

Artificial intelligence can mean many things. I focus just on one. Computer programs are becoming more helpful. They start working for us not just as stupid machines, but almost as partners.

In partnership, it is extremely important that partner understands your intentions and plans.

Can we express in computer terms what we want to do?

This is a big “if”, which is gradually dissolving into “how”.

There are new approaches that appear almost daily in the growing world of Semantic Technologies.

But, as we will see a bit later, tools are not as helpful without the fundamental knowledge, which we luck today.

Information Technology is looking for developers with Big Data, Semantic Technology and Cognitive Computing skills, but colleges and universities still offer Visual Basic and C++ programming.

No questions, developers still need to know programming languages. We have hundreds of them today.

Which one to learn? Almost all Big Data and Artificial Intelligent products, including the famous IBM Watson, are created in Java, which can easily run on any platform, from mainframe to mobile phones.

While teaching at University of Phoenix and DeVry University, I suggested changes to the curriculum, but any change take years for accredited schools. Only price change can be done quickly there.

But there are valid alternatives to enormously expensive education in colleges and universities.

Read more about educational problems and a suggested solution at [15]

Online study is helping many people across the world in the learning process. There are several websites such as, Udemy and Total Training, which provide access to thousands of course libraries.

However, most of online schools have the following common limitations.

  • One approach for all students

All students are different, which is why one single tutorial on a subject might not be able to answer all the questions that each individual student might have. This can become quite a problem for those who need to ask a question, but do not have the right resources and left to their devices to find answers.

  • Too Many Useless Connections

When investing time to study from a tutorial, students realize that it had little to no relevance to the subject matter – or added very little value to their knowledge.

  • No Hands-On Experience

Online tutorials in most cases hardly give students any exposure to real-life projects, just providing simple, often not even complete, examples.

To combat these issues, many students are now opting for different resources such as Internet Technology Summit Program [13],, where they can get their personalized guidance from an experienced instructor and work in small teams on real projects.

The curriculum provides a selected set of courses, which is building the ladder to the latest internet technologies. The ladder starts with the soft skills, such as art of Critical Thinking and Communications, important pre-requisites to Knowledge Engineering and to the science of Integrated Software and Knowledge Architecture. Find more on these skills here: [13].

The biggest benefit of online training is still there, students can study at their convenient time with their personal pace, while working under guidance of an experience instructor, who cares about the final destination for a student, – getting a job in the new field, where newcomers are welcomed as a developer, consultant, or a start-up entrepreneur.

Critical Thinking and Communications skills help developers to clearly express their ideas.

Knowledge Engineering and Knowledge Architecture subjects prepare a bridge for building systems with Knowledge-Driven Architecture [4]. Knowledge-Driven Architecture is the way of architecting systems based on business rules and situational scenarios. Today we can see new software frameworks, such as Google Robot Framework or Cucumber, discussed in the review “Top Development Skills” [12].

Growing importance of communications skills is gradually changing demography of a development crowd. Women, who naturally communicate more than men, have an advantage here.

Find more about the upcoming changes in the article about Women and Men in IT and management: [16].

In the beginning was the Word…



  1. Cycorp combines an unparalleled common sense ontology and knowledge base with a powerful reasoning engine and natural language interfaces,
  2. Financial Industry Business Ontology (FIBO) standard,
  3. Conversational Semantic Service Map, Yefim (Jeff) Zhuk, The system for collaborative design, assembly on-the-fly, execution, benchmarking, and negotiation of computer services and applications by developers and subject matter experts, US Patent Pending.
  4. Knowledge-Driven Architecture, Yefim Zhuk, Streamlining development and driving applications with business rules & scenarios, US Patent,
  5. The book online, “IT of the future”,, focuses on practical steps to transition the current IT of competing applications to a unified Semantic Cloud Architecture and describes Business Architecture Sandbox for Enterprise.
  6. Adaptive Robot System with Knowledge-Driven Architecture, Yefim Zhuk, On-the-fly translations of situational requirements into adaptive robot skills, US Patent,
  7. MuleSoft Enterprise Service Buse (ESB),
  8. Apache ActiveMQ,
  9. Distributed Knowledge and Process system, Yefim Zhuk, The system allows negotiate multiple forms of collaboration, and contains sufficiently flexible levels of data security for online collaboration, US Patent,
  10. Collaborative Security and Decision Making, Yefim Zhuk, transforming a beautiful idea of collaborative security decision making into a working system, US Patent,
  11. Rules Collector system, Yefim Zhuk, Transforming “tribal knowledge” into formal rules to drive applications and business processes, US Patent,
  12. Top Development Skills – review,
  13. Internet Technology Summit Program – integrated software and knowledge engineering,
  14. Galvanize IT – teaching Data Science,
  15. – a problem and a solution – upcoming changes in management

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