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Semantic

Modelling

An Ontology Engineering course focuses on how to design, build, and use ontologies—formal representations of knowledge used in systems like the Semantic Web and knowledge graphs.

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Course Detail

The course focuses focuses on ontologies and the Semantic Web, enabling intelligent systems to process, share, and reason about data effectively.

Course Features

  • Ontology Engineering
  • Foundations of Ontologies
  • Knowledge Representation
  • Lexical Sense

Lexsense

Semantic Search

Lexsense is a research-driven platform focused on helping computers understand language, meaning, and context. It works at the intersection of AI, linguistics, and data science, especially in fields like NLP, semantic search, and machine learning. 

  • 20 hours on-demand video
  • Full Lifetime Access
  • Access on Mobile and TV
  • Certificate of Completion

Course Content

11 sections • 28 lecture • 19h 33m total length

Ontology Engineering is the process of designing structured representations of knowledge using concepts, relationships, and rules. It enables machines to interpret, share, and reason about data in intelligent systems.

This covers the basic principles behind ontologies, including types, structure, and design philosophies. It helps define how real-world entities and their relationships are formally modeled.

Knowledge Representation focuses on encoding information in a form that computers can process and reason about. It uses logical models to represent facts, rules, and relationships within a domain.

This area involves standards like RDF, OWL, and SPARQL that enable data to be linked and understood across the web. It supports machine-readable data and interoperability between systems.

It describes the steps involved in creating an ontology, from planning and design to implementation and evaluation. It often follows structured methodologies to ensure consistency and reusability.

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    Designer
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