Knowledge Representation and Reasoning (KRR) is a field in artificial intelligence (AI) that focuses on how to represent information in a way that a computer system can use to make decisions with human-like reasoning.
Knowledge representation involves structuring information in a form that a computer can understand. A way to do this is by using ontologies or knowledge graphs, which allows for relationships and hierarchies within your data to be represented.
Reasoning refers to the process of drawing conclusions, making inferences, and solving problems based on the information in the knowledge graph. With a reasoning engine, these logical operations can be performed on the represented knowledge to derive new information.
KRR systems are more expressive than traditional databases so make it easier to model complex knowledge. Greater flexibility in the way data is structured makes KRR ideal to represent real world relationships and concepts.
Reasoning transforms this data into valuable information, both to the benefit of performance and insights that can be extracted. With incremental reasoning this happens automatically which allows for any new changes and updates to the database to be handled effortlessly and optimally, saving time and maintenance costs.
The synergy between knowledge representation and reasoning is crucial in building systems that can understand, infer, and act upon information in a structured and rule-based manner.