The Semantic Web: RPI ITWS Capstone (Fall 2012)
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Overview/introduction to the Semantic Web for RPI ITWS Capstone class (Fall 2012)

Overview/introduction to the Semantic Web for RPI ITWS Capstone class (Fall 2012)

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The Semantic Web: RPI ITWS Capstone (Fall 2012) Presentation Transcript

  • 1. ITWS Capstone Lecture:The Semantic Web John S. Erickson, Ph.D. Director, Web Science Operations Tetherless World Constellation RPI
  • 2. ...the purpose of the lecture is to summarize the Semantic Web with key concepts and the introduction of a few advanced ideasthat will be useful to these graduating seniors in grad school or their careers... Greg Hughes
  • 3. ...the purpose of the lecture is to summarize the Semantic Web with key concepts and the introduction of a few advanced ideasthat will be useful to these graduating seniors in grad school or their careers...
  • 4. Boil the ocean!
  • 5. What really matters?
  • 6. Is this “Semantic Web” for real?
  • 7. 1989...
  • 8. 1989...““Vague but exciting...” Vague but exciting...”
  • 9. 2001...
  • 10. 2001...
  • 11. 2001...
  • 12. 2011...
  • 13. 2011...
  • 14. 2011...
  • 15. 2011...
  • 16. 2011...
  • 17. 2011...
  • 18. 2011...
  • 19. Percent of total catalogs(from 192 catalogs) 19 19
  • 20. Intl Open Govt Dataset Search:Percent of total catalogs searching 1,022,787 datasets(from 192 catalogs) from 192 catalogs in 24 languages representing 43 countries and international organizations (Summer 2012) 20 20
  • 21. 2012...
  • 22. 2012...
  • 23. Semantic Web?
  • 24. Semantic Web?“Web of meaning”
  • 25. Semantic Web?“Web of meaning” Make meaningful Make meaningful assertions assertions about things about things on the Web... on the Web... Web of Data
  • 26. Semantic Web? “Web of meaning” Web of DataLink ideas...Link ideas... Linked Data
  • 27. Assertions...
  • 28. ...about ideas???
  • 29. subjectsubject predicate object object
  • 30. subjectsubject predicate object objectarticlearticle has creator Jim Jim
  • 31. doi:10.1109/MC.2009.30 http://purl.org/dc/elements/1.1/ creator http://dbpedia.org/resource/James_Hendler http://dbpedia.org/resource/James_Hendler
  • 32. doi:10.1109/MC.2009.30 http://purl.org/dc/elements/1.1/ creator http://dbpedia.org/resource/James_Hendler http://dbpedia.org/resource/James_Hendler
  • 33. Thats how to describe things...
  • 34. ...but how do we find things?
  • 35. SPARQL:pattern matchingover RDF graphs
  • 36. http://bit.ly/RumkhW http://bit.ly/RumkhW?s?s dbpedia2:blackboard ?blackboard ?blackboard
  • 37. http://bit.ly/Rumtlp http://bit.ly/Rumtlp?s?s dbpedia2:blackboard ?blackboard ?blackboard
  • 38. http://bit.ly/RumQwuhttp://bit.ly/RumQwu
  • 39. http://bit.ly/RumQwu http://bit.ly/RumQwu “There is no such month?s?s dbpedia2:blackboard “There is no such month as “Rocktober” as “Rocktober”
  • 40. http://bit.ly/RumQwuhttp://bit.ly/RumQwu
  • 41. http://bit.ly/RumQwu http://bit.ly/RumQwuhttp://dbpedia.org/resource/Double,_Double,_Boy_in_Troublehttp://dbpedia.org/resource/Double,_Double,_Boy_in_Trouble
  • 42. When in 2009 The Inventor said unto us...
  • 43. Use URIs as names for thingsUse URIs as names for thingsUse HTTP URIs so that people can look up Use HTTP URIs so that people can look upthose names (on the Web) those names (on the Web)When someone looks up a URI, return When someone looks up a URI, returnuseful information, using the standards useful information, using the standards((RDF*,SPARQL)) RDF*, SPARQLInclude links to other URIs, so that they can Include links to other URIs, so that they candiscover more things discover more things
  • 44. Use URIs as names for thingsUse URIs as names for thingsUse HTTP URIs so that people can look up Use HTTP URIs so that people can look upthose names (on the Web) those names (on the Web)When someone looks up a URI, return When someone looks up a URI, returnuseful information, using the standards useful information, using the standards((RDF*,SPARQL)) RDF*, SPARQLInclude links to other URIs, so that they can Include links to other URIs, so that they candiscover more things discover more things
  • 45. The Linked Data CloudThe Linked Data Cloud
  • 46. The Linked Data CloudThe Linked Data Cloud
  • 47. The Linked Data CloudThe Linked Data Cloud
  • 48. How does this help us?
  • 49. Linked Data enables agile data integration andapplication creation
  • 50. Mashup: OrgPedia Open Corporate DataMashup: OrgPedia Open Corporate Data http://tw.rpi.edu/orgpedia/
  • 51. Mashup: RPI Research CentersMashup: RPI Research Centers
  • 52. Mashup: RPI Research CentersMashup: RPI Research Centers
  • 53. Mashup: Research DataMashup: Research Data
  • 54. Mashup: Research DataMashup: Research Data
  • 55. Example: Extending a Sci Publishing PortalExample: Extending a Sci Publishing Portal
  • 56. Example: Extending a Sci Publishing PortalExample: Extending a Sci Publishing Portal
  • 57. Idea: Linking Data-driven Apps with “Smart Content” http://inference-web.org/wiki/Semantic_Water_Quality_Portal http://inference-web.org/wiki/Semantic_Water_Quality_Portal
  • 58. [data integration/data science]
  • 59. Schematic for Deep Carbon Virtual Observatory and Interoperability SemanticIntegrated Discovery interoperability Analytics Global Census, Virtual visualizations and mining Mineral Laboratory, ...Applications Application-level mediation: vocabulary, mapping to science and data terms SemanticSoftware, Deep Energy/ interoperability Physics/ …. Res/Flux Life Chemistry ApplicationsTools & Apps Applications Models Semantic query, Q uery, hypothsis and access and inference use of data Semantic mediation: physics, chemistry, mineral, emission data - ChemML, Metadata, Data schema, Emission/ data GVP MINDAT EOS EarthChem Compositions Repositories ... ... ...
  • 60. ...the purpose of the lecture is to summarize the Semantic Web with key concepts and the introduction of a few advanced ideasthat will be useful to these graduating seniors in grad school or their careers...
  • 61. Semantic Web key concepts... RDF, SPARQL, Linked Data, mashups, dataviz,RDFa, microformats, Schema.org
  • 62. Semantic Web key concepts... advanced ideas... RDF, SPARQL, ontology, inference, Linked Data, reasoning, provenance, mashups, dataviz, machine learning,RDFa, microformats, policy-based systems Schema.org
  • 63. Semantic Web key concepts... advanced ideas... RDF, SPARQL, ontology, inference, Linked Data, reasoning, provenance, mashups, dataviz, machine learning,RDFa, microformats, policy-based systems Schema.org careers...
  • 64. ????