Overview

As per the course website, "This Unit focuses on equipping you with the practical skills to be able to design solutions for knowledge representation and reasoning problems. You will be introduced to a systematic approach to exploring, understanding and extracting knowledge from data sources for modelling knowledge-based systems."

My Reflection

Overall Reflection

This unit was mainly about getting hands-on experience with Protégé, which is an open-source ontology editor and knowledge management system developed by Stanford University. The unit included a seminar, in which the tutor discussed ideas around ontology modelling, in addition to a set of readings and a formative activity to follow the infamous pizza ontology tutorial.

Readings Reflection

The reading that I found most interesting is an article titled What is a Knowledge Representation? by Davis, Shrobe and Szolovits (1993), which discussed knowledge representation through detailing its five main roles, and connected it to a wider background of difference in its logical view and psychological view, also arguing that representation and reasoning are intertwined and inseparable. This made me think and relate knowledge representation to a wider context, even if unrelated to computer science directly. For example, I started thinking about ideologies and belief systems as forms of knowledge representation that shape how individuals and societies reason about the world and make decisions. I also thought about how then time and its material conditions shape evolving frames of knowledge, and therefore frames of knowledge can each be though of as a domain of possible ontologies. These would then be the possible lenses through which societies and individuals interpret and reason about their realities in a given era, with its own conditions, constraints and possibilities. I wondered then if there can be a superior meta domain (like the owl:Thing class in OWL) that encapsulates all possible frames of knowledge and ontologies across time and contexts.

Below are some quotes that I especially like from the reading:

• "If, as we have argued, all representations are imperfect approximations to reality, each approximation attending to some things and ignoring others, then in selecting any representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world. That is, selecting a representation means making a set of ontological commitments."
• "Note that at each layer, even the first (e.g., selecting rules or frames), the choices being made are about representation, not data structures. Part of what makes a language representational is that it carries meaning, i.e., there is a correspondence between its constructs and things in the external world. That correspondence in turn carries with it constraint."
• "From a purely mechanistic view, reasoning in machines (and somewhat more debatably, in people) is a computational process. Simply put, to use a representation we must compute with it. As a result, questions about computational efficiency are inevitably central to the notion of representation."
• "Representation and reasoning are inextricably intertwined: we cannot talk about one without also, unavoidably, discussing the other. We argue as well that the attempt to deal with representation as knowledge content alone leads to an incomplete conception of the task of building an intelligent reasoner."

Reference

Davis, R., Shrobe, H. and Szolovits, P. (1993) ‘What is a Knowledge Representation?’, AI Magazine, pp. 17–33. Available at: https://groups.csail.mit.edu/medg/ftp/psz/k-rep.html (Accessed: 14 December 2025).