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Module V: Web 3.0

  1. Q1(j). The primary goal of the Semantic Web is to make web information (i) More understandable and processable by machines through structured semantics (ii) Restricted to static HTML documents (iii) Available only through email (iv) Independent of any representation of meaning20262m

    Module V: Web 3.0

    The primary goal of the Semantic Web is to make web information
    (i) More understandable and processable by machines through structured semantics
    (ii) Restricted to static HTML documents
    (iii) Available only through email
    (iv) Independent of any representation of meaning

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    Worked Solution

    Correct answer: (i) More understandable and processable by machines through structured semantics. The Semantic Web represents data and relationships in a structured, machine-interpretable way.

  2. Q8(b). Discuss the Semantic Web and explain how it improves the way information is represented and processed on the Web.20267m

    Module V: Web 3.0

    Discuss the Semantic Web and explain how it improves the way information is represented and processed on the Web.

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    Worked Solution

    Semantic Web

    The Semantic Web is an approach to structuring web data so that software can interpret the meaning and relationships of information, rather than treating pages only as text for people to read.

    Key technologies

    • RDF (Resource Description Framework): represents facts as subject–predicate–object triples. Example: (Course, taughtBy, Teacher).
    • URI/IRI: identifies resources.
    • Ontologies and OWL: define concepts, categories, properties and constraints in a domain.
    • SPARQL: queries RDF data.
    • Linked Data: connects structured datasets through shared identifiers and relationships.

    How it improves the Web

    1. Machine-readable meaning: data includes explicit relationships.
    2. Interoperability: different systems can share data using common vocabularies.
    3. Integration: facts from multiple sources can be linked and queried.
    4. Improved search: applications can search by entities and relationships, not just keywords.
    5. Reasoning: ontology rules can help infer relationships or detect inconsistencies.
    6. Intelligent applications: supports knowledge graphs, data integration and semantic recommendations.

    Example: a university can represent that a course is taught by a teacher, belongs to a department and has prerequisites. An application can query those relationships even if the data comes from separate systems.

    Challenges: creating and maintaining good metadata, agreeing on vocabularies, data quality, privacy and the cost of integration. Semantic Web technology does not automatically make all web content intelligent; data must be represented and maintained appropriately.

    Conclusion: RDF, ontologies, OWL and SPARQL add machine-processable structure and meaning to web data, improving discovery and integration.

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