ITWS Capstone Lecture (Spring 2013)

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ITWS Capstone Lecture (Spring 2013)

  1. 1. ITWS Capstone Lecture:The Semantic Web John S. Erickson, Ph.D. Director, Web Science Operations Tetherless World Constellation RPI
  2. 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...
  3. 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. 4. Boil the ocean!
  5. 5. What really matters?
  6. 6. Is this “Semantic Web” for real?
  7. 7. 1989...
  8. 8. ““Vague but exciting...” Vague but exciting...” 1989...
  9. 9. 2001...
  10. 10. 2001...
  11. 11. 2001...
  12. 12. Today...
  13. 13. Today...
  14. 14. Today...
  15. 15. Today...
  16. 16. Today...
  17. 17. Today...
  18. 18. Today...
  19. 19. Today...
  20. 20. Percent of total catalogs(from 192 catalogs) 20 20
  21. 21. 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) 21 21
  22. 22. Today...
  23. 23. 2012...
  24. 24. Semantic Web?
  25. 25. Semantic Web?“Web of meaning”
  26. 26. Semantic Web?“Web of meaning” Make meaningful Make meaningful assertions assertions about things about things on the Web... on the Web... Web of Data
  27. 27. Semantic Web? “Web of meaning” Web of DataLink ideas...Link ideas... Linked Data
  28. 28. Assertions...
  29. 29. ...about ideas???
  30. 30. subjectsubject predicate object object
  31. 31. subjectsubject predicate object objectarticlearticle has creator Jim Jim
  32. 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. 33. 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
  34. 34. Thats how to describe things...
  35. 35. ...but how do we find things?
  36. 36. SPARQL:pattern matchingover RDF graphs
  37. 37. http://bit.ly/RumkhW http://bit.ly/RumkhW?s?s dbpedia2:blackboard ?blackboard ?blackboard
  38. 38. http://bit.ly/Rumtlp http://bit.ly/Rumtlp?s?s dbpedia2:blackboard ?blackboard ?blackboard
  39. 39. http://bit.ly/RumQwuhttp://bit.ly/RumQwu
  40. 40. 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”
  41. 41. http://bit.ly/RumQwuhttp://bit.ly/RumQwu
  42. 42. http://bit.ly/RumQwu http://bit.ly/RumQwuhttp://dbpedia.org/resource/Double,_Double,_Boy_in_Troublehttp://dbpedia.org/resource/Double,_Double,_Boy_in_Trouble
  43. 43. When in 2009 The Inventor said unto us...
  44. 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. 45. 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
  46. 46. The Linked Data CloudThe Linked Data Cloud
  47. 47. The Linked Data CloudThe Linked Data Cloud
  48. 48. The Linked Data CloudThe Linked Data Cloud
  49. 49. How does this help us?
  50. 50. Linked Data enables agile data integration andapplication creation
  51. 51. Mashup: OrgPedia Open Corporate DataMashup: OrgPedia Open Corporate Data http://tw.rpi.edu/orgpedia/
  52. 52. Mashup: RPI Research CentersMashup: RPI Research Centers
  53. 53. Mashup: RPI Research CentersMashup: RPI Research Centers
  54. 54. Mashup: Research DataMashup: Research Data
  55. 55. Linked Data Publication: HHS Health DataLinked Data Publication: HHS Health Data
  56. 56. Linked Data Publication: HHS Health DataLinked Data Publication: HHS Health Data
  57. 57. Linked Data Publication: HHS Health DataLinked Data Publication: HHS Health Data
  58. 58. Linked Data Publication: HHS Health DataLinked Data Publication: HHS Health Data
  59. 59. Linked Data Publication: HHS Health DataLinked Data Publication: HHS Health Data
  60. 60. Example: Extending a Sci Publishing PortalExample: Extending a Sci Publishing Portal
  61. 61. 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
  62. 62. [data integration/data science]
  63. 63. 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, Query, 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 ... ... ...
  64. 64. [data integration/data science]
  65. 65. ...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...
  66. 66. Semantic Web key concepts... RDF, SPARQL, Linked Data, mashups, dataviz,RDFa, microformats, Schema.org
  67. 67. 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
  68. 68. 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...
  69. 69. ????

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