Brain-Inspired Expertise Management

< SMWCon Fall 2012
SMWCon Fall 2012Brain-Inspired Expertise Management
SMWCon Fall 2012
Brain-Inspired Expertise Management
Talk details
Description: Using SMW and brain-inspired expertise management ontology for expertise management
Speaker(s): Hans de Bruin
Slides: see here
Type: Presentation
Audience: Everyone
Event start: 26 Oct 2012 14:50
Event finish: 26 Oct 2012 15:15
Length: 25 minutes
Video: click here
Give feedback

Expertise is recognized as an important asset for any organization. It can be defined as the extensive knowledge and abilities that persons possess in particular areas of study based on research, experience and occupation, which set them apart from other persons operating in these areas. It is generally accepted that knowledge can be divided in explicit and implicit knowledge. The former kind of knowledge includes the theoretical understanding of a subject (knowing that knowledge), whereas the latter refers to practical skills (knowing how knowledge). Expertise management is about capturing and disseminating the skills possessed by experts, that is, the know-how to perform activities for achieving goals.

Just as knowledge needs to be managed, the same applies for expertise. In this article, we will show how expertise can be managed, including explicit as well as implicit knowledge. Frequently, implicit (skill) knowledge is equated with tacit knowledge, that is, knowledge that is possessed by experts, but cannot be explained, and therefore cannot be managed. However, this is not true in general. Take for example learning how to drive a car. Clearly, driving a car cannot be learned from just an instruction book. Instead, a novice seeks help from an experienced instructor, who shows the good practices and also the pitfalls to avoid. At first, a novice needs all its attention to master the driving practices, but after a while, the practices become more and more internalized. Eventually, the practices can be executed without thinking consciously. The conclusion is that implicit (skill) knowledge can be transferred from the initiated to the uninitiated. However, further practice is needed to become a real expert. In practice this means that organizations can be turned into learning organizations by sharing explicit and to a certain extent implicit knowledge thereby accelerating the learning process of individuals.

The process of sharing knowledge is grounded in the Soft Systems Methodology (SSM). The result is an Expertise Management Method (EMM), specifically addressing expertise management issues. It is beyond the scope of this article to go into more detail. Nevertheless, a few remarks are in order here. First of all, expertise need not only be shared, but need also be valuated. For instance, a seasoned employee leaving a company has gained expertise on particular subjects. The question is whether this expertise has value for the company or not. It might be the case that the expertise is outdated due to changing working methods. Generally speaking, this means that expertise must be valuated by peers. In essence, this is a group learning process, providing a way to turn an organization into a learning organization. Second, not only good practices need to be captured, but also bad practices. Learning form mistakes is essential to learning.

Once knowledge has been shared and valuated, the knowledge must be shaped in such a way that it can be disseminated. To this end, we have developed a brain-inspired expertise management ontology (EMont) that captures both explicit and implicit knowledge. EMont is based on the latest insights in the working of the human brain, stemming from diverse disciplines, including cognitive psychology, artificial intelligence, computer science, and philosophy. In particular, the speculative, but for our purposes very useful, Memory Prediction Framework (MPF) is used to capture implicit (skill) knowledge. MPF states that we think about the (near) future, whether consciously or not, by making analogies with past experiences. In a way, we predict the future. Another source for EMont is situating knowledge in context and using the cognitive coherence theory within a context for finding solutions for given problems.

The overall architecture of EMont is based on a flexible building block approach with which the various insights can be tied together in a consistent way. EMont has been implemented in SMW. The combination of a wiki and a semantic database makes SMW a good vehicle for capturing and disseminating expertise.