AI, KRR, and the Semantic Web

Winter 2019
Instructor: Raghava Mutharaju
IIIT Delhi

Artificial Intelligence

  • It is the study of the general principles of building intelligent agents.
  • An agent is any device that can perceive its environment through sensors and react to it by taking action to achieve a stated goal.
  • Mimicking human senses, i.e., sight, sound, touch, smell, taste, is a form of perception.
  • Intelligent agents should be able to interpret and process other forms of input such as text, semi-structured, and structured data.
  • Output of an intelligent agent could be in multiple forms such as movement (reaching a destination), decision taken, sound etc.
  • Several applications of AI that have not only improved our day-to-day lives but help in saving lives
    • Google Maps
    • Web Search
    • Intelligent Assistants (Siri, Cortana etc.)
    • Targeted advertising
    • Diagnosis of diseases
    • Autonomous vehicles
    • Playing games (Jeopardy, Chess, Go etc.)
    • Robots

Subfields of AI

  • Planning
  • Natural Language Processing
  • Learning (Machine Learning)
  • Computer Vision
  • Robotics
  • Knowledge Representation and Reasoning
  • Artificial Neural Networks

Knowledge Representation and Reasoning (KRR)

  • Techniques to capture knowledge about the world in a form that machines can understand
    • A Car is a type of Vehicle
    • Car has exactly four wheels
    • Car has at least two doors and at most four doors
  • Reasoning is the process of deriving new facts (knowledge) based on existing facts
    • All birds fly
    • Pigeon is a bird
    • Can Pigeon fly?
  • KRR is the field of AI that helps an agent to use what it knows (background knowledge) to decide what to do

KRR Formalisms

  • Different mechanisms to capture knowledge and reason over it
    • Frames
    • Semantic Nets
    • Logic
      • First Order Logic
      • Description Logics
      • Ontologies
      • Resource Description Framework (RDF)

Semantic Web

Three Themes

  1. Building Models
    • Describe the world in abstract terms to simplify its understanding
  2. Computing with Knowledge
    • Machines that can do logical deduction/inference from encoded knowledge in order to draw meaningful conclusions
  3. Exchanging Information
    • Transmission of complex information between machines that allows distribution, interlinking, and reconciliation of knowledge
    • RSS - some versions of RSS use RDF

Enabling Technologies

  • Purpose is to provide structure to the Web and to the data in general
  • Move from web of documents to web of data (Linked Data)
  • Semantic Web technologies and W3C standards
    • RDF (Resource Description Framework)
    • OWL (Web Ontology Language)
    • SPARQL (query language)
    • SHACL (Shapes Constraint Language)

Linked Data

Structured Data (RDF) Demo

RDF Graph, Property Graph, Knowledge Graph

RDF Graph

  • Triples that describe any resource
  • <Delhi> <capitalOf> <India>
  • Triples are directed labelled graphs

Property Graph

Property Graph
Image source:

Knowledge Graph

Knowledge Graph
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Knowledge Graph

  • There is no standard definition of Knowledge Graphs
  • It is a graph that captures knowledge in the form of entities, relationships between them, properties, and additional information including provenance
    • "Things" not strings. Things should have semantics.
    • Eg: What does it mean to be a "Person", "Organization", etc.
    • Things are entities that have properties and are connected by relationships


  • Knowledge Graphs are used in several domains and by several commercial enterprises
    • Healthcare
    • Geoscience
    • Industrial domains such as manufacturing, power, oil and gas
    • Web Search
    • Recommender systems
    • Conversational agents (chatbots, QA systems)
    • Google, Microsoft, Amazon, Ebay, LinkedIn, GE, Accenture etc.

Gartner's Hype Cycle

Gartner's Hype Cycle of Emerging Technologies, 2018
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  • Textbook: Foundations of Semantic Web Technologies. Pascal Hitzler et. al. CRC Press.
  • Reference book: Artificial Intelligence. Stuart Russell, Peter Norvig. Pearson
  • Reference book: Knowledge Representation and Reasoning. Ronald Brachman, Hector Levesque. Morgan Kaufmann