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The Semantic Web: RPI ITWS Capstone (Fall 2012)
The document summarizes a lecture on the Semantic Web, with the purpose of introducing key concepts and some advanced ideas that would be useful for graduating seniors. The lecture covers the evolution of the Semantic Web from its origins in 1989, and explains core concepts like RDF, SPARQL, Linked Data, and how they enable applications like mashups and data visualization. Some advanced ideas discussed include ontology, inference, reasoning and machine learning.
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The Semantic Web: RPI ITWS Capstone (Fall 2012)
- 1. ITWS Capstone Lecture: TheSemantic Web John S. Erickson, Ph.D. Director, Web Science Operations Tetherless World Constellation RPI
- 2. ...the purpose ofthe 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... Greg Hughes
- 3. ...the purpose ofthe 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...
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- 6. Is this “SemanticWeb” for real?
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- 8. 1989... ““Vague but exciting...” Vague but exciting...”
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- 19. Percent of totalcatalogs (from 192 catalogs) 19 19
- 20. Int'l Open Gov'tDataset 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
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- 24. Semantic Web? “Web ofmeaning”
- 25. Semantic Web? “Web ofmeaning” 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 Data Link ideas... Link ideas... Linked Data
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- 29. subject subject predicate object object
- 30. subject subject predicate object object article article 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
- 34. That's how todescribe things...
- 35. ...but how dowe find things?
- 36.
- 37. http://bit.ly/RumkhW http://bit.ly/RumkhW ?s ?s dbpedia2:blackboard ?blackboard ?blackboard
- 38. http://bit.ly/Rumtlp http://bit.ly/Rumtlp ?s ?s dbpedia2:blackboard ?blackboard ?blackboard
- 40.
- 41. 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”
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- 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. When in 2009The Inventor said unto us...
- 46. Use URIs asnames 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. Use URIs asnames 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
- 48. The Linked DataCloud The Linked Data Cloud
- 49. The Linked DataCloud The Linked Data Cloud
- 50. The Linked DataCloud The Linked Data Cloud
- 53. How does this help us?
- 54. Linked Data enables agile data integration and application creation
- 55. Mashup: OrgPedia OpenCorporate Data Mashup: OrgPedia Open Corporate Data http://tw.rpi.edu/orgpedia/
- 56. Mashup: RPI ResearchCenters Mashup: RPI Research Centers
- 57. Mashup: RPI ResearchCenters Mashup: RPI Research Centers
- 58. Mashup: Research Data Mashup:Research Data
- 59. Mashup: Research Data Mashup:Research Data
- 60. Example: Extending aSci Publishing Portal Example: Extending a Sci Publishing Portal
- 61. Example: Extending aSci Publishing Portal Example: Extending a Sci Publishing Portal
- 64. Idea: Linking Data-drivenApps with “Smart Content” http://inference-web.org/wiki/Semantic_Water_Quality_Portal http://inference-web.org/wiki/Semantic_Water_Quality_Portal
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- 66. Schematic for DeepCarbon Virtual Observatory and Interoperability Semantic Integrated Discovery interoperability Analytics Global Census, Virtual visualizations and mining Mineral Laboratory, ... Applications Application-level mediation: vocabulary, mapping to science and data terms Semantic Software, Deep Energy/ interoperability Physics/ …. Res/Flux Life Chemistry Applications Tools & 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 ... ... ...
- 67. ...the purpose ofthe 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...
- 68. Semantic Web key concepts... RDF, SPARQL, Linked Data, mashups, dataviz, RDFa, microformats, Schema.org
- 69. 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
- 70. 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...
- 79.