KNOWLEDGE REPRESENTATION AND REASONING

What this module is about

Knowledge Representation and Reasoning (KRR) involves the understanding of the mechanisms responsible for intelligent behaviour, and this is an essential part of Artificial Intelligence. Intelligent behaviour can be achieved by processing large amounts of knowledge. As Nabel (2015) points out, to be able to understand and produce natural language relies heavily on knowledge about the language, about the structure of the world and the social construct, including the relationships that exist between the world and language. This is particularly true when one wants to be able to communicate with machines. The semantic web is an emerging technology that can be used to develop solutions to enable machines and humans to communicate effectively using natural language.

For humans to be able to communicate with machines, the meaning of statements expressed in a natural language need to be preserved regardless of the way it is expressed. An example is how we would express the statement “I love Food”. Humans can choose to express it as “I Food”. The meaning of these two statements comes to us naturally as humans, but not to machines because of the syntax used in the two statements. KRR techniques such as ontologies can be used to model the semantics of statements that enable machines to understand. The semantic web is a network of large ontologies, and these are what are implemented in many applications including knowledge management systems, decision support systems or knowledge portals (Tarasov, 2019).

During this module, we will cover the concepts and principles of KRR. We will review methods and frameworks as well as tools to enable you to engage with, experience and envision current as well as future developments in the area KRR as sub-discipline of AI. You will not only be equipped with theoretical and practical skills, but this module will also make you aware of the practical applications of KRR as a key part of AI solutions.

Key learning objectives include:

  1. Critique the need for formal approaches to knowledge representation and reasoning.
  2. Review critically properties of a knowledge-based system.
  3. Appraise critically modelling techniques for knowledge representation and reasoning.
  4. Examine and incorporate different modelling approaches to solving KRR problems.

References

  1. Nebel, B. (2015) ‘Logics for Knowledge Representation’, in: Wright, J. (ed)', International Encyclopedia of the Social & Behavioral Sciences, 2(14), pp. 319–321.
  2. Tarasov, V., Seigerroth, U. and Sandkuhl, K. (2019) 'Ontology Development Strategies in Industrial Contexts', in: Abramowicz, W.& Paschke, A. (ed) Business Information Systems Workshops. BIS 2018. Lecture Notes in Business Information Processing, 339, pp. 156 - 167.