Knowledge Representation in AI involves the formal encoding of information about the world in a computationally tractable format that enables reasoning, inference, and decision-making. Approaches include symbolic systems (first-order logic, description logics, semantic networks), graph-based representations (knowledge graphs, ontologies), probabilistic models (Bayesian networks, Markov logic networks), and distributed representations (embeddings, neural-symbolic integration).
Semantic Classification
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Key Characteristics
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Formalizes entities, relationships, and constraints
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Supports logical inference and automated reasoning
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Enables knowledge sharing and interoperability
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Integrates structured and unstructured knowledge sources
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Facilitates explainability and interpretability
Overview
Knowledge Representation in AI involves the formal encoding of information about the world in a computationally tractable format that enables reasoning, inference, and decision-making. Approaches include symbolic systems (first-order logic, description logics, semantic networks), graph-based representations (knowledge graphs, ontologies), probabilistic models (Bayesian networks, Markov logic networks), and distributed representations (embeddings, neural symbolic integration). Modern knowledge representation combines symbolic and subsymbolic methods, enabling systems to perform logical reasoning while learning from data.
Related Concepts
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References
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Sowa, J. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole.
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Hogan, A. et al. (2021). Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge, Morgan & Claypool.
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Hamilton, W. et al. (2017). Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin, 40(3), 52-74.