Abdulhakim Bashir
My E-Portfolio based on work carried out on my Msc Program on Artificial Intelligence and Machine Learning at the University of Essex.
Collaborative Learning Discussion 2
Initial Post
by Abdulhakim Bashir - Sunday, 20 July 2025, 8:17 PM
In Nasim’s (2022) thesis, an ontology is defined as a formal, shared model of a domain that enables structured data to be exchanged and understood by software agents. Ontologies play a key role in the Semantic Web, where data is no longer just readable but also interpretable by machines.
Formal approaches to knowledge representation and reasoning (KRR) are essential for enabling systems to infer new information, validate logic, and interact intelligently. Brachman and Levesque (2004) argue that without formal logic, systems can’t reason beyond surface-level data. However, formal methods can be complex and resource-intensive, especially in fast-changing domains.
When it comes to expressing ontologies on the web, OWL 2 stands out. It’s expressive enough to define classes, relationships, and constraints, and it supports automated reasoning through description logic (Hitzler et al., 2012). While RDF provides the basic structure and OWL Lite is easier to use, they don’t offer the same reasoning power. KIF, although powerful, lacks web adoption and standard support.
Knowledge-based systems are valued for their ability to explain reasoning, update knowledge modularly, and separate domain expertise from application logic (Russell & Norvig, 2021). Still, they can become brittle if knowledge is hardcoded without mechanisms for learning or adaptation.
Different modelling techniques come with trade-offs. Logical approaches are rigorous but inflexible. Ontologies offer a middle ground and formality with practical tools. Increasingly, hybrid models that combine symbolic reasoning with machine learning are showing promise (Lake et al., 2017).
Overall, while formal KRR isn’t always easy to implement, it’s critical for building trustworthy, interoperable systems on the web.
References
Brachman, R.J., & Levesque, H.J. (2004). Knowledge Representation and Reasoning. Morgan Kaufmann.
Hitzler, P. et al. (2012). OWL 2 Web Ontology Language Primer. W3C.
Lake, B.M., Ullman, T.D., Tenenbaum, J.B., & Gershman, S.J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40.
Nasim, T.M. (2022). Improving Ontology Alignment Using Machine Learning Techniques. MSc Thesis, Arizona State University.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
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