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Understanding Ontology Support for the Semantic Web: Concepts and Technologies

This comprehensive overview details the role of ontology in the Semantic Web, emphasizing the significance of RDF, DAML/OIL, and UPML in structuring and interlinking data. It explores various diagrams and examples illustrating the application of ontologies for enhanced interoperability, consistency, and validation. The document also delves into the innovative characteristics of smart agents, SPARQL queries, and the implications of flexible database designs in the era of semantic portals and the Internet of Things. Gain insights into how these components contribute to a smarter web.

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Understanding Ontology Support for the Semantic Web: Concepts and Technologies

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  1. Ontology support For the Semantic Web

  2. The big picture • Diagram, page 9 • html5 • xml can be used as a syntactic model for RDF and DAML/OIL • RDF, RDF Schema (with data modeling) – RDF takes object specifications and flattens them into triples • DAML/OIL – used to specify the details of UPML components • UPML – architectural description language for components, adapters, connection configurations

  3. DAML & OIL • DAML examples, pages 69 to 77 • OIL examples, pages 99 • OIL constraints 101 to 103 • Intriguing diagram, page 113

  4. UPML • Diagram of UPML’s role, page 144 • Key function: “component markup” • UPML diagram, page 147 – a PSM is a “problem solving method” • Protégé is a free editor for ontology-related languages, page 160 & 162

  5. Another Big view of the semantic web • Diagram, page 173 • Intriguing comparison diagram, page 175 • Extra capabilities of ontologies over lower level specifications • Consistency • Filling in semantic details • Interoperability support • Validation and verification • Configuration support • Support for structured searches • Generalization/specialization meta information

  6. Interesting twist on how databases should be built • Old way – page 266 • New way – page 268 • The smarter DB architecture, page 273 • What are we adding? • Used to be data, schema, then sql, then transaction manager, then apps, then UI • Now we are introducing more metadata? More schema? • Or is this a completely different kind of database? • Where data consists of assertions?

  7. A “semantic portal” • Page 320 • Both humans and “agents” can access semantic portals • But how do humans interact with a semantic portal via a browser? • Comparison between ontologies and knowledge – page 322 • The idea of extensibility as a critical aspect of the semantic web • Not just new data, not just new metadata, but new inferences as well • Big picture diagram, page 333

  8. Semantic Gadgets concept • Making smarts ubiquitous • The Internet of Things and Ambient Intelligence • For learning, mobile activities, using remote services • Mobile computing and mobile-based queries • Devices that can interact with our devices • Museum locations and user with sound device • Hand held devices and grocery store shopping and conganitively disabled

  9. Semantic annotation concept • Diagram – page 406 • Detailed diagram – page 415 • Example – pages 417 and 418 • We see the use of parallel databases that hold metadata that is searchable • And metadata can be applied in a personalized way to provide specific results to specific users • See page 420……..

  10. Task-achieving agents notion • Diagram, page 434 • Kinds of tasks • Automated planning • Computer-supported cooperative work • Multi-agent mixed-initiative planning • Workflow support • Example diagram, page 442 • This is a common way of viewing the new web • Smart agents replace browsers

  11. A concrete component: SPARQL • Query language modeled after SQL • It can walk through semantic websites and across semantic websites • SPARQL thus creates new knowledge by creating inferences that can cross website boundaries

  12. From - http://www.cambridgesemantics.com/2008/09/sparql-by-example/ • A SPARQL query comprises, in order: • Prefix declarations, for abbreviating URIs • Dataset definition, stating what RDF graph(s) are being queried • A result clause, identifying what information to return from the query • The query pattern, specifying what to query for in the underlying dataset • Query modifiers, slicing, ordering, and otherwise rearranging query results

  13. What can sparql do? • It can extend an ontology by adding new inferences as assertions • Retrieve triples that describe something • Ask true or false questions based on assertions

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