Transferring human knowledge to machines – knowledge engineering
Have you ever heard of knowledge engineering?
Automotive engineers test and optimize prototypes of automobiles, civil engineers develop technical solutions for construction projects and knowledge engineers construct knowledge bases, which enable machines algorithm to think.
Let’s have a closer look on knowledge engineer’s key task.
So instead of starting your day in a factory, slipping into a blue overall, knowledge engineers put themselves into their personal construction site, called the office.
As in many engineering fields, a great solution is derived from a challenging problem. More specific in our case: customers data problems.
As you might have guessed before, safety goggles are always a good idea when working with big data challenges. In doing so, knowledge engineers collect relevant information, organize data, develop knowledge bases and thereby most important connect all steps within a smart software.
But instead of screws or bolts – several techniques from artificial intelligence and computer science are available in their “mental” toolbox. These tools serve in a broad scope of application such as in financial, medical or automotive domains.
By generating machine manipulable domain models –ontologies– knowledge engineers tackle complicated and time-consuming data problems, which usually require the expertise of domain specialists – and that’s what knowledge engineers become in the end.
Knowledge engineers juggle with these problems and provide solutions for describing, implementing, distributing and visualizing customers data and transform it purposefully into knowledge. With that in mind, this method of engineering helps to detect and describe coherences in customers data and enables greater potential questioning of real-world problems.
Therefore tricky issues can be broken down in their complexity and in greater detail in order to support customers decision making in their project, which might offer a competitive advantage to the customers.
“Knowledge is only a potential power“ without application (Napoleon Hill). In certain cases, it needs an engineer with his toolbox to unfold its smart forces.
Source: Napoleon Hill (2007). “Your Magic Power to be Rich!”, p.62, Penguin
Author: Anna Richert
Published: July 24, 2018
Image rights: Pexels
Original blog post: https://www.yukkalab.com/transferring-human-knowledge-to-machines-knowledge-engineering/