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Ontology support

Ontology support. For the Semantic Web. The big picture. Diagram, page 9 h tml5 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

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Ontology support

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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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