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Understanding Knowledge Representation in Artificial Intelligence by Prof. Pushpak Bhattacharya

In this insightful lecture by Prof. Pushpak Bhattacharya, we explore the concept of Knowledge Representation (KR) in Artificial Intelligence. The session discusses the four layers of KR, its structured and unstructured forms, and the role of ontology/taxonomy in organizing knowledge. Topics include applications of KR in linguistic and visual situations, and examples of semantic networks and frames. The impact of upper ontologies, such as CYC and IEEE SUMO, on emerging technologies like the Semantic Web is also highlighted, demonstrating KR's critical role in AI and information processing.

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Understanding Knowledge Representation in Artificial Intelligence by Prof. Pushpak Bhattacharya

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Presentation Transcript

  1. CS344 Artificial Intelligence Prof. Pushpak Bhattacharya Class on 26 Mar 2007

  2. Knowledge Representation (KR) • AI is often equated with KR • Four layers in KR – Each layer knows how to use the layer below it. Wisdom Knowledge Information Data

  3. KR (contd.) • KR (kinds): • Structured (Semantic Net, Frame) • Unstructured (propositional and predicate calculus) • KR (application to): • Language situation (Linguistic processing) • Visual situations (Geometric processing) • KR (characterization): • Analytic (Problem solving) • Synthetic (Creative Situations, Arts)

  4. Ontology/Taxonomy • Ontology/Taxonomy is at the heart of KR • Hierarchical organization of concepts (typical relation is IS-A) • Terminology: • “Upper Ontology” – CYC project (D. Lenat – 1985) • IEEE SUMO (Standard Upper Merged Ontology) • Semantic Web efforts: Resource Description Framework (RDF), Web Ontology Language (OWL), Description Logic Entity Events Things Physical Abstract Concrete Mental Upper Ontology

  5. Structured Knowledge Representation • Structured Knowledge Representation is of two types, viz., Semantic Net and Frame • Semantic Net • Concepts • Relations • IS-A • PART-OF

  6. Knowledge Representation • Illustration through • Visual situation • Language situation Observer Scene ID of scene: Picture 101

  7. Unstructured Knowledge Representation • Visual situation – Unstructured knowledge representation for Picture 101: near(table, Ram) on(table, vase) in(table, flower) colour(flower, red)

  8. Structured Knowledge Representation Still Life Picture-101 IS-A • Representation of “Still Life” as SemanticNet is shown alongside. • Storing as records: Picture-101{ Instance_of: Still_life Has-parts: table-59{ instance_of: table} vase-3112{ instance_of: vase} ⋮ } Picture-101 PART-OF Ram PART-OF PART-OF RED PART-OF COLOUR-OF Table-59 Vase-3112 Flower-7123 ON IN

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