Linked data and its role in the semantic web Dave Reynolds, Epimorphics Ltd @der42
Roadmap image: Leo Oosterloo @ flickr.com What is linked data? Examples Modelling Access Strengths and weaknesses other to...
Linked data intro
<ul><li>Linked data ... </li></ul><ul><li>publishing data on the web ... </li></ul><ul><li>... to enable  integration ,  l...
Can’t we just publish data as files? <ul><li>pdf </li></ul><ul><ul><li>easy to read and publish  </li></ul></ul><ul><li>E...
Linked data <ul><li>Apply the principles of the web to publication of data </li></ul><ul><li>The web : </li></ul><ul><ul><...
Linked data <ul><li>Apply the principles to the web to publication of data </li></ul><ul><li>The  linked data  web : </li>...
Example schools information http://education.data.gov.uk/id/school/401874
Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” “ Secondary” “ Cardiff” l...
Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://sta...
Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://sta...
Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://sta...
 
Linked data principles <ul><li>Use URIs as names for things </li></ul><ul><li>Use HTTP URIs so that people can look up tho...
Linked open data cloud: 2007 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
Linked open data cloud: 2009 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
Linked open data cloud: 2010 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
Data.gov.uk – linked datasets and APIs
Data.gov.uk visualizations on top of linked data
 
Ordnance survey
Environment agency  - data, API, visualizations
BBC – integration and site design
E-commerce and rich snippets Overstock.com Peek-cloppenburg.de
Internal use
Open? <ul><li>Linked open data  </li></ul><ul><li>=  </li></ul><ul><li>linked data  </li></ul><ul><li>+  </li></ul><ul><li...
Modelling
Modelling Thing, entity, concept ... resource <ul><li>resource  being described </li></ul><ul><ul><li>abstract concept </l...
Modelling – RDF  – Resource Description Framework Statement, triple, logical assertion Subject Predicate Object
Modelling – RDF Statement, triple, logical assertion Subject Predicate Object some school has a name/label some literal
Modelling – RDF Statement, triple, logical assertion Subject Predicate Object http://education.data.gov.uk/id/school/40187...
Modelling – RDF Statement, triple, logical assertion Subject Predicate Object http://education.data.gov.uk/id/school/40187...
Modelling – RDF Statement, triple, logical assertion where school: =  http://education.data.gov.uk/id/school/ rdfs:  = htt...
Modelling – RDF Statement, triple, logical assertion Subject Predicate Object school:401874 rdfs:label “ Cardiff High Scho...
Modelling – RDF Statement, triple, logical assertion school:401874 “ Cardiff High School” ont:districtAdministrative la:00...
Modelling – RDF Statement, triple, logical assertion Subject Predicate Object school:401874 rdfs:label “ Cardiff High Scho...
RDF Syntaxes <ul><li>RDF/XML </li></ul><ul><ul><li>normative </li></ul></ul><ul><li>Turtle </li></ul><ul><ul><li>more huma...
Modelling – RDF RDF/XML syntax <rdf:RDF xmlns:rdf=&quot;http://www.w3.org/1999/02/22-rdf-syntax-ns#&quot; xmlns:ont=&quot;...
Modelling – RDF Turtle syntax @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix school: <http://education.da...
Modelling Vocabularies <ul><li>so far no actual models, let alone semantics </li></ul><ul><li>want to define </li></ul><ul...
Modelling – vocabularies Logical modelling <ul><li>modelling the domain, not a particular data structure </li></ul><ul><ul...
Modelling – vocabularies <ul><li>unfamiliar terminology but related to </li></ul><ul><ul><li>information architecture and ...
Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>ont:School rdfs:C...
Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>ont:WelshEstablis...
Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>school:401874 ont...
Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>school:401874 ont...
Modelling – RDFS RDF vocabulary description language <ul><li>properties, property hierarchy </li></ul>school:401874 person...
Modelling – RDFS RDF vocabulary description language <ul><li>class/property relations </li></ul><ul><ul><li>domain </li></...
Modelling - OWL <ul><li>richer modelling and semantics </li></ul><ul><li>axioms on properties </li></ul><ul><ul><li>transi...
Modelling – OWL <ul><li>supports much richer modelling </li></ul><ul><li>consistency checking of model </li></ul><ul><li>c...
Modelling Spectrum of goals and styles <ul><li>Lightweight vocabularies </li></ul><ul><li>Rich ontological models </li></u...
Modelling Ontology reuse <ul><li>invest in complete ontology for a domain </li></ul><ul><ul><li>rich but general model, ma...
Accessing all this data <ul><li>link following </li></ul><ul><ul><li>HTTP GET, follow links, aggregate relevant statements...
SPARQL <ul><li>core idea is pattern matching </li></ul><ul><ul><li>graph patterns with variables </li></ul></ul><ul><ul><l...
Accessing all this data <ul><li>link following </li></ul><ul><ul><li>HTTP GET, follow links, aggregate relevant statements...
Semantic web layer cake
Strengths and weaknesses image: spcbrass @ flickr.com
Strengths <ul><li>data integration </li></ul><ul><ul><li>use of global identifiers (URIs) </li></ul></ul><ul><ul><li>compo...
Weaknesses <ul><li>complexity of the stack </li></ul><ul><ul><li>alphabet soup – RDF, RDFS, OWL, SPARQL, RIF .. </li></ul>...
Wrapping up image: erika g. @ flickr.com
Things we missed out <ul><li>RDF nuances </li></ul><ul><ul><li>blank nodes, containers and collections </li></ul></ul><ul>...
Hot topics <ul><li>Government linked data </li></ul><ul><ul><li>identifiers to seed linked data </li></ul></ul><ul><ul><li...
fin.
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Case study: Local government payments data model publish use
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Introduction to linked data and the semantic web

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Talk on linked data for the BCS Data Management Specialist Group

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Introduction to linked data and the semantic web

  1. 1. Linked data and its role in the semantic web Dave Reynolds, Epimorphics Ltd @der42
  2. 2. Roadmap image: Leo Oosterloo @ flickr.com What is linked data? Examples Modelling Access Strengths and weaknesses other topics
  3. 3. Linked data intro
  4. 4. <ul><li>Linked data ... </li></ul><ul><li>publishing data on the web ... </li></ul><ul><li>... to enable integration , linking and reuse across silos </li></ul>
  5. 5. Can’t we just publish data as files? <ul><li>pdf </li></ul><ul><ul><li>easy to read and publish  </li></ul></ul><ul><li>Excel </li></ul><ul><ul><li>allows further processing and analysis  </li></ul></ul><ul><li>csv </li></ul><ul><ul><li>processing without need for proprietary tools  </li></ul></ul><ul><li>But ... </li></ul><ul><ul><li>structure of data not explained </li></ul></ul><ul><ul><li>no connection between different data sets, silos </li></ul></ul><ul><ul><li>static and fixed – can’t retrieve just slices relevant to problem </li></ul></ul>
  6. 6. Linked data <ul><li>Apply the principles of the web to publication of data </li></ul><ul><li>The web : </li></ul><ul><ul><li>is a global network of pages </li></ul></ul><ul><ul><li>each identified by a URL </li></ul></ul><ul><ul><li>fetching a URL gives a document </li></ul></ul><ul><ul><li>pages connected by links </li></ul></ul><ul><ul><li>open, anyone can say anything about anything else </li></ul></ul>
  7. 7. Linked data <ul><li>Apply the principles to the web to publication of data </li></ul><ul><li>The linked data web : </li></ul><ul><ul><li>is a global network of things </li></ul></ul><ul><ul><li>each identified by a URI </li></ul></ul><ul><ul><li>fetching a URI gives a set of statements </li></ul></ul><ul><ul><li>things connected by typed links </li></ul></ul><ul><ul><li>open, anyone can say anything about anything else </li></ul></ul><ul><li>Linked data is “data you can click on” </li></ul> 
  8. 8. Example schools information http://education.data.gov.uk/id/school/401874
  9. 9. Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” “ Secondary” “ Cardiff” label phase district
  10. 10. Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://statistics.data.gov.uk/id/local-authority-district/00PT “ Cardiff” label school:PhaseOfEducation_Secondary label
  11. 11. Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://statistics.data.gov.uk/id/local-authority-district/00PT “ Cardiff” label school:PhaseOfEducation_Secondary http://data.ordnancesurvey.co.uk/id/7000000000025484 label contains ward extent contains parish GML: 310499.4 184176.6 310476.5   ...
  12. 12. Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://statistics.data.gov.uk/id/local-authority-district/00PT “ Cardiff” label school:PhaseOfEducation_Secondary http://data.ordnancesurvey.co.uk/id/7000000000025484 label contains ward extent contains parish GML: 310499.4 184176.6 310476.5   ... same as
  13. 14. Linked data principles <ul><li>Use URIs as names for things </li></ul><ul><li>Use HTTP URIs so that people can look up those names </li></ul><ul><li>When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) </li></ul><ul><li>Include links to other URIs, so that they can discover more things </li></ul>Pattern of application of semantic web stack
  14. 15. Linked open data cloud: 2007 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
  15. 16. Linked open data cloud: 2009 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
  16. 17. Linked open data cloud: 2010 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
  17. 18. Data.gov.uk – linked datasets and APIs
  18. 19. Data.gov.uk visualizations on top of linked data
  19. 21. Ordnance survey
  20. 22. Environment agency - data, API, visualizations
  21. 23. BBC – integration and site design
  22. 24. E-commerce and rich snippets Overstock.com Peek-cloppenburg.de
  23. 25. Internal use
  24. 26. Open? <ul><li>Linked open data </li></ul><ul><li>= </li></ul><ul><li>linked data </li></ul><ul><li>+ </li></ul><ul><li>open data </li></ul>
  25. 27. Modelling
  26. 28. Modelling Thing, entity, concept ... resource <ul><li>resource being described </li></ul><ul><ul><li>abstract concept </li></ul></ul><ul><ul><li>real world thing </li></ul></ul><ul><ul><li>data item, particular measurement </li></ul></ul><ul><ul><li>document </li></ul></ul><ul><li>identify by URI </li></ul><ul><li>provide information making statements about those resources </li></ul><ul><li>identifier NOT a container c.f. UML </li></ul><ul><ul><li>open schema </li></ul></ul><ul><ul><li>critical to open extensibility and integration </li></ul></ul><ul><ul><li>similar to Entity-Attribute-Value modelling </li></ul></ul>
  27. 29. Modelling – RDF – Resource Description Framework Statement, triple, logical assertion Subject Predicate Object
  28. 30. Modelling – RDF Statement, triple, logical assertion Subject Predicate Object some school has a name/label some literal
  29. 31. Modelling – RDF Statement, triple, logical assertion Subject Predicate Object http://education.data.gov.uk/id/school/401874 has a name/label “ Cardiff High School”
  30. 32. Modelling – RDF Statement, triple, logical assertion Subject Predicate Object http://education.data.gov.uk/id/school/401874 http://www.w3.org/2000/01/rdf-schema#label “ Cardiff High School”
  31. 33. Modelling – RDF Statement, triple, logical assertion where school: = http://education.data.gov.uk/id/school/ rdfs: = http://www.w3.org/2000/01/rdf-schema# Subject Predicate Object school:401874 rdfs:label “ Cardiff High School”
  32. 34. Modelling – RDF Statement, triple, logical assertion Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label Cardiff
  33. 35. Modelling – RDF Statement, triple, logical assertion school:401874 “ Cardiff High School” ont:districtAdministrative la:00PT “ Cardiff” rdfs:label rdfs:label Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff”
  34. 36. Modelling – RDF Statement, triple, logical assertion Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff” la: 00PT rdfs:label “ Caerdydd”@cy
  35. 37. RDF Syntaxes <ul><li>RDF/XML </li></ul><ul><ul><li>normative </li></ul></ul><ul><li>Turtle </li></ul><ul><ul><li>more human readable/writable </li></ul></ul><ul><ul><li>being standardized </li></ul></ul><ul><li>RDFa </li></ul><ul><ul><li>embed in (X)HTML </li></ul></ul><ul><li>[others omitted] </li></ul>
  36. 38. Modelling – RDF RDF/XML syntax <rdf:RDF xmlns:rdf=&quot;http://www.w3.org/1999/02/22-rdf-syntax-ns#&quot; xmlns:ont=&quot;http://education.data.gov.uk/def/school/&quot; xmlns:la=&quot;http://statistics.data.gov.uk/id/local-authority-district/&quot; xmlns:school=&quot;http://education.data.gov.uk/id/school/&quot; xmlns:rdfs=&quot;http://www.w3.org/2000/01/rdf-schema#&quot;> <rdf:Description rdf:about=&quot;http://education.data.gov.uk/id/school/401874&quot;> <rdfs:label>Cardiff High School</rdfs:label> <ont:districtAdministrative> <rdf:Description rdf:about=&quot;http://statistics.data.gov.uk/id/local-authority-district/00PT&quot;> <rdfs:label>Cardiff</rdfs:label> </rdf:Description> </ont:districtAdministrative> </rdf:Description> </rdf:RDF> Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff”
  37. 39. Modelling – RDF Turtle syntax @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix school: <http://education.data.gov.uk/id/school/> . @prefix ont: <http://education.data.gov.uk/def/school/> . @prefix la: <http://statistics.data.gov.uk/id/local-authority-district/> . school:401874 rdfs:label &quot;Cardiff High School&quot;; ont:districtAdministrative la:00PT . la:00PT rdfs:label &quot;Cardiff&quot; . Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff”
  38. 40. Modelling Vocabularies <ul><li>so far no actual models, let alone semantics </li></ul><ul><li>want to define </li></ul><ul><ul><li>types of thing : Class </li></ul></ul><ul><ul><li>what you can say about them : Property </li></ul></ul><ul><li>encode definitions in more RDF and publish at the corresponding URIs </li></ul><ul><ul><li>link from data to data model </li></ul></ul><ul><ul><li>reuse published vocabularies to enable integration </li></ul></ul><ul><ul><li>freely combine different vocabularies or new ones </li></ul></ul>
  39. 41. Modelling – vocabularies Logical modelling <ul><li>modelling the domain, not a particular data structure </li></ul><ul><ul><li>what exists </li></ul></ul><ul><ul><li>what is asserted? what can you deduce from that? </li></ul></ul><ul><ul><li>not about constraints as such </li></ul></ul><ul><ul><li>monotonic, open world </li></ul></ul>controlled vocabulary taxonomy thesaurus ontology O ntology
  40. 42. Modelling – vocabularies <ul><li>unfamiliar terminology but related to </li></ul><ul><ul><li>information architecture and conceptual modelling </li></ul></ul><ul><ul><li>domain-driven design </li></ul></ul><ul><ul><li>... and yes knowledge representation </li></ul></ul>
  41. 43. Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>ont:School rdfs:Class rdf:type “ School” rdfs:label
  42. 44. Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>ont:WelshEstablishment ont:School rdfs:Class rdf:type rdf:type rdfs:subClassOf “ School” rdfs:label
  43. 45. Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>school:401874 ont:WelshEstablishment ont:WelshEstablishment ont:School rdfs:Class rdf:type rdf:type rdf:type rdfs:subClassOf “ School” rdfs:label
  44. 46. Modelling – RDFS RDF vocabulary description language <ul><li>classes, types and type hierarchy </li></ul>school:401874 ont:WelshEstablishment ont:WelshEstablishment ont:School rdfs:Class rdf:type rdf:type rdf:type rdfs:subClassOf school:401874 ont:WelshEstablishment ont:School rdf:type  “ School” rdfs:label “ School” rdfs:label
  45. 47. Modelling – RDFS RDF vocabulary description language <ul><li>properties, property hierarchy </li></ul>school:401874 person:JoeBloggs ont:staffAt ont:headOf rdf:Property ont:headOf rdf:type rdfs:subPropertyOf  school:401874 person:JoeBloggs ont:staffAt ont:headOf
  46. 48. Modelling – RDFS RDF vocabulary description language <ul><li>class/property relations </li></ul><ul><ul><li>domain </li></ul></ul><ul><ul><li>range </li></ul></ul><ul><li>Already have power to do some vocabulary mapping </li></ul><ul><ul><li>declare classes or properties from different vocabularies to be equivalent: </li></ul></ul><ul><ul><li>A rdfs:subClassOf B </li></ul></ul><ul><ul><li>B rdfs:subClassOf A </li></ul></ul>
  47. 49. Modelling - OWL <ul><li>richer modelling and semantics </li></ul><ul><li>axioms on properties </li></ul><ul><ul><li>transitive, symmetric, inverseOf, ... </li></ul></ul><ul><ul><li>functional, inverse functional </li></ul></ul><ul><ul><li>equivalent property </li></ul></ul><ul><li>axioms on classes </li></ul><ul><ul><li>intersection, union, disjoint, equivalent </li></ul></ul><ul><li>restrictions on classes </li></ul><ul><ul><li>some value from, all values from, cardinality, has value, one of, keys </li></ul></ul><ul><li>axioms on individuals </li></ul><ul><ul><li>same as, different from, all different </li></ul></ul><ul><li>imports </li></ul>
  48. 50. Modelling – OWL <ul><li>supports much richer modelling </li></ul><ul><li>consistency checking of model </li></ul><ul><li>consistency checking of data </li></ul><ul><ul><li>some surprises if used to schema languages </li></ul></ul><ul><ul><li>open world, no unique name assumption </li></ul></ul><ul><ul><li>can extend to closed world checking </li></ul></ul><ul><li>inference </li></ul><ul><ul><li>classification </li></ul></ul><ul><ul><li>inferred relationships </li></ul></ul>
  49. 51. Modelling Spectrum of goals and styles <ul><li>Lightweight vocabularies </li></ul><ul><li>Rich ontological models </li></ul><ul><li>simple modelling </li></ul><ul><li>just enough agreement to get useful work done </li></ul><ul><li>removing boundaries to enable information to be found and connected </li></ul><ul><li>global consistency not possible </li></ul><ul><li>a little semantics goes a long way </li></ul><ul><li>rich domain models </li></ul><ul><li>need expressivity </li></ul><ul><li>consistency is critical </li></ul><ul><li>make complex inferences you can rely on, across data you trust </li></ul><ul><li>knowledge is power </li></ul>
  50. 52. Modelling Ontology reuse <ul><li>invest in complete ontology for a domain </li></ul><ul><ul><li>rich but general model, may be modular inside </li></ul></ul><ul><ul><li>strong “ontological commitment” </li></ul></ul><ul><ul><li>e.g. medical ontologies </li></ul></ul><ul><li>reuse small, common, vocabularies </li></ul><ul><ul><li>FOAF, SIOC, Dublin Core, Org ... </li></ul></ul><ul><ul><li>pick and choose classes and properties you need </li></ul></ul><ul><ul><li>fill in a few missing links for your domain </li></ul></ul><ul><li>generic reusable vocabularies </li></ul><ul><ul><li>Data cube vocabulary </li></ul></ul>
  51. 53. Accessing all this data <ul><li>link following </li></ul><ul><ul><li>HTTP GET, follow links, aggregate relevant statements </li></ul></ul><ul><li>query </li></ul><ul><ul><li>SPARQL </li></ul></ul>
  52. 54. SPARQL <ul><li>core idea is pattern matching </li></ul><ul><ul><li>graph patterns with variables </li></ul></ul><ul><ul><li>any subgraph which matches yields row of bindings </li></ul></ul><ul><li>syntax based on Turtle syntax for RDF </li></ul><ul><li>web API endpoints </li></ul><ul><li>lots of power </li></ul>rdfs:label ont:districtAdministrative ?school [ ] “ Cardiff” <ul><li>filters </li></ul><ul><li>optionals </li></ul><ul><li>named graphs </li></ul><ul><li>sub-queries </li></ul><ul><li>property chains </li></ul><ul><li>aggregation </li></ul><ul><li>federated query </li></ul><ul><li>update </li></ul><ul><li>construct </li></ul>
  53. 55. Accessing all this data <ul><li>link following </li></ul><ul><ul><li>HTTP GET, follow links, aggregate relevant statements </li></ul></ul><ul><li>query </li></ul><ul><ul><li>SPARQL </li></ul></ul><ul><li>linked data API </li></ul><ul><ul><li>RESTful API onto linked data resources </li></ul></ul><ul><ul><li>simple query, usable without RDF stack, web dev friendly </li></ul></ul><ul><ul><li>easy to layer visualizations and UIs on top </li></ul></ul><ul><li>third parties </li></ul><ul><ul><li>search engines and aggregators e.g. Sindice, sameAs.org </li></ul></ul>
  54. 56. Semantic web layer cake
  55. 57. Strengths and weaknesses image: spcbrass @ flickr.com
  56. 58. Strengths <ul><li>data integration </li></ul><ul><ul><li>use of global identifiers (URIs) </li></ul></ul><ul><ul><li>composable – statements v. containers, schemaless </li></ul></ul><ul><ul><li>linking, vocabulary mapping </li></ul></ul><ul><li>extensible, incremental, decentralized, resilient </li></ul><ul><ul><li>no global ontology/schema to develop or maintain </li></ul></ul><ul><ul><li>freely add terms from other vocabularies </li></ul></ul><ul><ul><li>open world assumption </li></ul></ul><ul><li>modelling and data entwined </li></ul><ul><ul><li>link data to models, data in context </li></ul></ul><ul><ul><li>use same technology to share, manage extend models </li></ul></ul><ul><li>supports inference and classification </li></ul><ul><li>rich access routes </li></ul><ul><ul><li>web linking, download, query, web APIs </li></ul></ul>
  57. 59. Weaknesses <ul><li>complexity of the stack </li></ul><ul><ul><li>alphabet soup – RDF, RDFS, OWL, SPARQL, RIF .. </li></ul></ul><ul><ul><li>unfamiliar “ontology”, “logical entailment” </li></ul></ul><ul><ul><li>lots of arcane details </li></ul></ul><ul><ul><li>RDF/XML syntax </li></ul></ul><ul><li>performance of schema-less stores </li></ul><ul><ul><li>optimization challenges </li></ul></ul><ul><li>limited validation and constraints </li></ul><ul><li>cost of modelling,ontology development </li></ul><ul><li>no inbuilt notions of time, uncertainty </li></ul><ul><li>use the parts you need </li></ul><ul><li>tooling e.g. Linked Data API </li></ul><ul><li>core ideas not that complex </li></ul><ul><li>technology improving steadily </li></ul><ul><li>hybrid solutions </li></ul><ul><li>closed world checkers </li></ul><ul><li>ontology reuse </li></ul><ul><li>generic ontologies (data cube) </li></ul><ul><li>tools </li></ul><ul><li>model on top </li></ul>
  58. 60. Wrapping up image: erika g. @ flickr.com
  59. 61. Things we missed out <ul><li>RDF nuances </li></ul><ul><ul><li>blank nodes, containers and collections </li></ul></ul><ul><ul><li>named graphs </li></ul></ul><ul><li>linked data nuances </li></ul><ul><ul><li>URI for thing v. web page, content negotiation, httprange-14 </li></ul></ul><ul><ul><li>URI architecture </li></ul></ul><ul><li>OWL nuances </li></ul><ul><ul><li>OWL species, serializations, lots of details </li></ul></ul><ul><li>Other technologies in the stack </li></ul><ul><ul><li>SPARQL update, rules (RIF), GRDDL, Powder, Geo SPARQL, RDB mapping, triple/quad stores </li></ul></ul><ul><li>Embedding structured data in markup </li></ul><ul><ul><li>RDFa, micro formats, micro data, schema.org and all that </li></ul></ul>
  60. 62. Hot topics <ul><li>Government linked data </li></ul><ul><ul><li>identifiers to seed linked data </li></ul></ul><ul><ul><li>data publication </li></ul></ul><ul><ul><ul><li>transparency, improving services, economic growth </li></ul></ul></ul><ul><li>structured data and search engines </li></ul><ul><ul><li>rich snippets, structured results, SEO </li></ul></ul><ul><ul><li>search => question answering </li></ul></ul><ul><li>user interfaces </li></ul><ul><ul><li>visualization, exploration, exploiting linking </li></ul></ul><ul><li>data as a service </li></ul>
  61. 63. fin.
  62. 64. Spare
  63. 65. Case study: Local government payments data model publish use
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