Representation-based

Relating to or relying on the use of symbolic or concrete representations to model, understand, or manipulate information and concepts. This approach emphasizes the creation and processing of structured data, often in the form of knowledge bases, graphs, or formal languages, to enable reasoning, problem-solving, and communication. It contrasts with approaches that may prioritize direct experience, connectionist models, or implicit knowledge. representation-based systems typically involve encoding, retrieval, and manipulation of these explicit representations. The efficiency and effectiveness of a representation-based system largely depend on the quality and appropriateness of the chosen representations.

Representation-based meaning with examples

• In the field of Artificial Intelligence, representation-based reasoning uses knowledge graphs to model relationships between entities. This allows the AI to draw inferences and answer complex questions. This approach emphasizes the importance of structured data and symbolic manipulation. For example, a representation-based system might infer that 'if x is a cat, and cats eat mice, then x eats mice.'
• A key feature of expert systems is that they are often representation-based, using rule-based systems or semantic networks. These knowledge representations allow for decision-making in specific domains. Consider a medical diagnosis system which stores symptoms and diseases in a graph; a representation-based system might map symptoms to possible diagnoses, and present these in a comprehensible format.
• Semantic web technologies employ representation-based frameworks to link and share data across different platforms. By using standardized languages like RDF and OWL, data can be understood and processed by machines. This approach enhances the accessibility and interoperability of information online. This supports the use of agents that understand meaning, leading to greater web accessibility and efficient information retrieval.
• In natural language processing, machine translation can utilize representation-based methods, where sentences are translated by converting them into abstract meaning representations. This approach is different from statistical methods relying on large data sets of translations. A representative use case would be encoding sentences into a common internal form before generating a translation in the target language, thereby resolving ambiguity.