Knowledge engineering is one of many complex problem solving methods helping companies build robust knowledge-based systems. Thanks to these decision support software tools, companies can address a number of knowledge-related issues that were previously only addressed by human experts.
Using Artificial Intelligence techniques, like Machine Learning and NLP, this powerful computer science tool helps improve knowledge acquisition processes throughout large and complex organizations.
In this post we discuss knowledge engineering and some of its fundamentals in case you are considering its implementation for your organization.
Knowledge Engineering Defined
Knowledge engineering is a HiTech tool used to emulate human decision processes. It has the potential to increase the availability of knowledge across organizations when human experts are scarce. In terms of computer science, it is a field of Artificial Intelligence that seeks to model decision making processes that are regularly performed by domain experts. By doing so, companies can make knowledge widely available throughout the organization, generating a series of benefits.
Limitations of Knowledge Engineering
Much has been said about the real scope of knowledge engineering. Although it is widely accepted that this field of computer science has not yet reached a major turning point, it has without a doubt made important advances in recent years.
Thanks to AI techniques like Machine Learning and NLP it has been able to move from knowledge transfer methods to more evolved modelling processes. As a problem solving method, transfer processes have often been regarded as being limited, as they do not consider certain human characteristics like intuition and gut feeling. Because computers do not operate in non-linear ways of thinking like humans do, these human characteristics have been hard to replicate.
Knowledge transfer processes have been replaced with modeling processes, a different approach with which computers can achieve the same results as domain experts, even if the road used to arrive to the same conclusions is not the same. In other words, knowledge modeling processes are a different method with which computers can reach the same conclusion that a human expert would. This approach helps organizations improve knowledge acquisition goals through algorithms. Nonetheless, knowledge modeling processes are still not perfect when it comes to emulating human decision making processes.
Benefits of Knowledge Engineering
The subject of knowledge and its nature goes back to the ancient Greeks. What exactly is knowledge is a question that humanity has not been able to answer, yet we have found ways to work with it.
Although knowledge is highly accessible and democratized throughout the world thanks to the Internet and other digital technologies, there are many fields that require a high degree of expertise. This occurs in fields as diverse as finance and medicine. For these, experts matter a lot.
Because knowledge can be scarce even the problem of accessing is solved, organizations need ways to increase its availability. By using knowledge engineering to build decision support software, companies can optimize their limited resources.
In general, the benefits of knowledge engineering are:
- Wider availability of knowledge.
- Increased speed of decision making.
- The possibility to replicate expert decision making.
- A scalable decision support software tool.
One of the main concerns surrounding knowledge engineering is that it can replace experts, having an important impact on society. This is a wrong approach to it. Instead of being a threat, this tool should be seen as a way to empower non-experts to make better decisions. Just like automation, its benefits outweigh its costs.
Knowledge Engineering: Principles
Within the scope of Knowledge Engineering academic literature, Knowledge Engineering: Principles and Methods is a go to paper that covers some of the most important principles of the subject. Although much has happened since its publication in 1998, it still holds some important information.
In general, these are some of the principles that drive knowledge engineering:
- The transfer process approach offers a limited account of how knowledge works.
- Expert knowledge expressed only in terms of logic is incomplete. Tacit knowledge matters.
- Modeling processes do not seek to create adequate cognitive models. They are an approximation of real-world behaviors.
- Models need to be revisable due to the dynamic nature of reality and subjective aspects of decision making.
Finding the right knowledge engineering team for your project can be hard. Not all companies possess the right tools to translate expert knowledge into an input that computers can use. However, this shouldn’t be a reason to demotivate your organization from implementing knowledge engineering features.
The best way is to start small by building decision support systems that can help your employees make better decisions without having to be experts. Decision making apps are a great way to start building expert systems. Thanks to cloud technologies, apps can access large databases that can help improve decision making processes. The actual challenge, rather than building the system itself, becomes finding the right app development company who can deliver what you need.
Humanity is still a long way from building decision support software that can replace experts, but even if they are not perfect, they can help improve knowledge related processes. Knowledge management, like many other business processes, needs to be addressed as a continuous and iterative process that can always be improved.