Overview

As per the course website, "This unit focuses on one of the steps in developing accurate and efficient knowledge-based systems. It covers concepts and principles of knowledge acquisition, and approaches, to knowledge acquisition and formalism."

My Reflection

Overall Reflection

This unit is composed of a lecturecast, a set of readings, and an assignment submission. The lecturecast and the readings overall introduced the topics of knowledge management and its relation to ontology, in light of formalism. This context extended my view for ontology, especially within business contexts to understand that an ontology is a way of formalism or knowledge representation, and might need to be preceded by knowledge acquisition through the means of information gathering, like interviews, observations, questionnaires, and document analysis.

The most notable reading for me wat the paper Knowledge elicitation techniques in a knowledge management context by Gavrilova and Andreeva (2012), especially for introducing me to an even new domain, which is knowledge engineering (KE), and bridging between it and knowledge management (KM). According to the paper, KM focuses on how organisations capture, share and use knowledge for competitive advantage, while KE focuses on how to to develop technical methods and systems to represent, formalise and apply knowledge. The two domains are connected, but seemingly there is a gap between the two, at least on an academic level. For example, according to the paper, KM often assumes proactive employees, while KE recognises the need for structured elicitation methods. Also, KE introduces the role of the analyst in knowledge elicitation while the role did was overlooked by KM literature.

As for the assignment, it was mainly about writing a 1500-word review article on the paper Ontology development for agriculture domain, analysing the authors' methodology for ontology development, identifying key application areas of similar ontologies, and suggest how their approach can be adopted in our domains. Personally, I didn't see the paper a very good choice, as it was not well-structured nor written, and the suggested ontology was not even complete. However, it gave me an opportunity to read and write on the relation between ontology and business intelligence, which is my domain, as I am still trying to figure out how ontology relates to my work, and how it relates specially to the practice of data modelling, and maybe data visualisation as well.