Knowledge Representation in Artificial Intelligence: Concepts, Evolution, Techniques, and Applications
Mohd Zahid M. S. 1, Sh-Hussain S. H. 1
1 Open University Malaysia, Malaysia
Symbolic artificial intelligence reasons transparently from explicit rules, but those rules must be authored by hand and cope poorly with unfamiliar cases. Statistical methods learn from data and generalise well, yet leave their knowledge distributed across parameters that cannot be inspected or edited. Hybrid systems combining the two are the common response to the limitations of each, and building one requires deciding which knowledge to hold explicitly and which to leave learned. Knowledge representation is the sub-field concerned with the first of these – encoding facts, concepts, relationships, and rules in a form a machine can store, interrogate, and reason over. This chapter treats those foundations in depth: the kinds of knowledge a system holds, the vocabulary of entities, relations, rules, and inference, and the tension between how much a representation can express and how quickly it can be reasoned over. That tension has shaped where machine knowledge is kept, moving from hand-authored rules to formal logic, then to shared ontologies and knowledge graphs, and lately into model parameters. Six families of technique are examined with worked examples: logic and rules, frames and ontologies, probabilistic representation, knowledge graphs, learned embeddings, and temporal and spatial representation. Deliberate representation is what allows a claim to be checked, updated, and traced to its source, even when a model can produce it from memory.
knowledge representation, ontology, knowledge graph, inference, hybrid AI, agentic systems.
APA
Mohd Zahid, M. S., & Sh-Hussain, S. H. (2027). Knowledge representation in artificial intelligence: Concepts, evolution, techniques, and applications. In I. S. Pypenko (Ed.), Artificial Intelligence in Digital Society, Vol. 2 (pp. 01–15). KRPOCH. https://doi.org/10.26697/aids.2027.2
Harvard
Mohd Zahid, M. S., & Sh-Hussain, S. H. "Knowledge representation in artificial intelligence: Concepts, evolution, techniques, and applications. In I. S. Pypenko (Ed.)" Artificial Intelligence in Digital Society, Vol. 2. [online] pp. 01–15. viewed 10 March 2027, https://culturehealth.org/books/Arhiv_DOI/aids/aids.2027.2.pdf Vancouver
Mohd Zahid, M. S., & Sh-Hussain, S. H. Knowledge representation in artificial intelligence: Concepts, evolution, techniques, and applications. In I. S. Pypenko (Ed.). Artificial Intelligence in Digital Society, Vol. 2. [Internet]. [cited 10 March 2027]; 01–15. Available from: https://doi.org/10.26697/aids.2027.2 https://culturehealth.org/books/Arhiv_DOI/aids/aids.2027.2.pdf
Mohd Zahid Mohd Soperi – https://orcid.org/0000-0001-5042-5793; PhD, Associate Professor, Faculty of Computing and Analytics, Open University Malaysia, Petaling Jaya, Selangor, Malaysia.