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