ITWS Capstone (RPI, Fall 2013)

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ITWS Capstone (RPI, Fall 2013)

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ITWS Capstone (RPI, Fall 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 ideas that 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 ideas that 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. Percent of total catalogs (from 192 catalogs) Int'l Open Gov't Dataset Search: searching 1,022,787 datasets 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” Web of Data Make meaningful Make meaningful assertions assertions about things about things on the Web... on the Web...
  27. 27. Semantic Web? “Web of meaning” Web of Data Link ideas... Link ideas... Linked Data
  28. 28. Assertions...
  29. 29. ...about ideas???
  30. 30. subject subject predicate object object
  31. 31. subject subject predicate object object ““article” article” “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. That's how to describe things...
  35. 35. ...but how do we find things?
  36. 36. SPARQL: pattern matching over RDF graphs
  37. 37. SPARQL: pattern matching over RDF graphs ““SPARQL Protocol SPARQL Protocol and and Query Language...” Query Language...”
  38. 38. http://bit.ly/RumkhW http://bit.ly/RumkhW ?s ?s dbpedia2:blackboard ?blackboard ?blackboard
  39. 39. http://bit.ly/Rumtlp http://bit.ly/Rumtlp ?s ?s dbpedia2:blackboard ?blackboard ?blackboard
  40. 40. http://bit.ly/RumQwu http://bit.ly/RumQwu
  41. 41. http://bit.ly/RumQwu http://bit.ly/RumQwu ?s ?s dbpedia2:blackboard “There is no such month “There is no such month as “Rocktober” as “Rocktober”
  42. 42. http://bit.ly/RumQwu http://bit.ly/RumQwu
  43. 43. http://bit.ly/RumQwu http://bit.ly/RumQwu http://dbpedia.org/resource/Double,_Double,_Boy_in_Trouble http://dbpedia.org/resource/Double,_Double,_Boy_in_Trouble
  44. 44. When in 2009 The Inventor said unto us...
  45. 45. Use URIs as names for things Use URIs as names for things Use HTTP URIs so that people can look up Use HTTP URIs so that people can look up those names (on the Web) those names (on the Web) When someone looks up a URI, return When someone looks up a URI, return useful information, using the standards useful information, using the standards ((RDF*,SPARQL)) RDF*, SPARQL Include links to other URIs, so that they can Include links to other URIs, so that they can discover more things discover more things
  46. 46. Use URIs as names for things Use URIs as names for things Use HTTP URIs so that people can look up Use HTTP URIs so that people can look up those names (on the Web) those names (on the Web) When someone looks up a URI, return When someone looks up a URI, return useful information, using the standards useful information, using the standards ((RDF*,SPARQL)) RDF*, SPARQL Include links to other URIs, so that they can Include links to other URIs, so that they can discover more things discover more things
  47. 47. The Linked Data Cloud The Linked Data Cloud
  48. 48. The Linked Data Cloud The Linked Data Cloud
  49. 49. The Linked Data Cloud The Linked Data Cloud
  50. 50. How does this help us?
  51. 51. Linked Data enables agile data integration and application creation
  52. 52. Mashup: OrgPedia Open Corporate Data Mashup: OrgPedia Open Corporate Data http://tw.rpi.edu/orgpedia/
  53. 53. Mashup: RPI Research Centers Mashup: RPI Research Centers
  54. 54. Mashup: RPI Research Centers Mashup: RPI Research Centers
  55. 55. Mashup: Research Data Mashup: Research Data
  56. 56. Linked Data Publication: HHS Health Data Linked Data Publication: HHS Health Data
  57. 57. Linked Data Publication: HHS Health Data Linked Data Publication: HHS Health Data
  58. 58. Linked Data Publication: HHS Health Data Linked Data Publication: HHS Health Data
  59. 59. Linked Data Publication: HHS Health Data Linked Data Publication: HHS Health Data
  60. 60. Linked Data Publication: HHS Health Data Linked Data Publication: HHS Health Data
  61. 61. Example: Extending a Sci Publishing Portal Example: Extending a Sci Publishing Portal
  62. 62. 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
  63. 63. [data integration/data science]
  64. 64. Schematic for Deep Carbon Virtual Observatory and Interoperability Integrated Applications Discovery visualizations Semantic interoperability Analytics and mining Global Census, Virtual Mineral Laboratory, ... Application-level mediation: vocabulary, mapping to science and data terms Software, Tools & Apps Deep Energy/ Life Applications Semantic interoperability Physics/ Chemistry Models Semantic query, hypothsis and inference …. Res/Flux Applications Query, access and use of data Semantic mediation: physics, chemistry, mineral, emission data - ChemML, Data Repositories GVP MINDAT EOS EarthChem Metadata, schema, data ... ... ... Emission/ Compositions
  65. 65. [data integration/data science]
  66. 66. ...the purpose of the lecture is to summarize the Semantic Web with key concepts and the introduction of a few advanced ideas that will be useful to these graduating seniors in grad school or their careers...
  67. 67. Semantic Web key concepts... RDF, SPARQL, Linked Data, mashups, dataviz, RDFa, microformats, Schema.org
  68. 68. Semantic Web key concepts... advanced ideas... RDF, SPARQL, Linked Data, mashups, dataviz, RDFa, microformats, Schema.org ontology, inference, reasoning, provenance, machine learning, policy-based systems
  69. 69. Semantic Web key concepts... advanced ideas... RDF, SPARQL, Linked Data, mashups, dataviz, RDFa, microformats, Schema.org ontology, inference, reasoning, provenance, machine learning, policy-based systems careers...
  70. 70. ????

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