Lecture linked data cloud & sparql

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  • Talk about benefits
  • http://datahub.io/dataset/bbc-musichttp://datahub.io/dataset/geonames-semantic-webDbpediaData.gov.uk statistics
  • http://datahub.io/dataset/bbc-musichttp://datahub.io/dataset/geonames-semantic-webDbpediaData.gov.uk statistics
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • #prefix declaration@prefix dbp-ont: <http://dbpedia.org/ontology/>.#result clauseSELECT *#dataset definitionFROM http://dbpedia.org#query patternWHERE {dbp-ont:Person ?p ?o.}
  • http://linkeddatabook.com/editions/1.0/#htoc6
  • http://linkeddatabook.com/editions/1.0/#htoc6
  • 6 Minute12 Minute

Transcript

  • 1. COMP3725Knowledge Enriched Information SystemsLecture 11-12: Linked Data & SPARQL Dhavalkumar Thakker (Dhaval) School of Computing, University of Leeds 1
  • 2. Reading & ReflectionsBizer, et al. Linked Data – The Story so far• What is Linked Data? – Is it same as Web of Data?• What excited you most about linked data while reading this article? OR what did you find most interesting?• Is Linked Data happening in real life? Have you seen this anywhere? 2
  • 3. Outline• What is Linked Data?• Why Linked Data?• How to publish as part of Linked Data – Linked Data Principles – Finding existing sources – Possible software architectures – Query Language: SPARQL 3
  • 4. Web of Documents 4
  • 5. Web of DocumentsAbout:•United States•Barack Obama•Presidential Election (Past)•Some relevance to currently held•Democrats & Republicans•Winner & Looser About:•Chicago •Location, Event, Places, Persons•Etc.. , Groups, Abstract concepts (winning, losing) 5
  • 6. ..people can parse documents and extract meaning 6
  • 7. The web of documents• Analogy – Global file system• Designed for – Human consumption• Primary objects – documents• Links between – documents (or sub-parts of)• Semantics – implicit 7
  • 8. The web of documents: Issues• Web of Documents but primarily About Data – But the connection is implicit• Integration & Querying – Show me all the news stories by US Presidents coming from Chicago? 8
  • 9. Semantic Web•We need to help machines to understand the web..somachines can help us to understand things.•If machines have access to the data about things (i.e.knowledge) then they can do better job while processingdocuments 9
  • 10. Linked Data Linking Things Thing Thing Thing Thing Thing Thing Thing Thing Thing Thing relationship relationship relationship relationship links links links links 10An introduction to Linked Data- Tim Heath, Talis
  • 11. Linked Data…• …. is about creating global database of linked things• …refers to a set of best practices for publishing and interlinking data on the Web…• ….is a method of publishing data [on the Web], so that it can be interlinked and become more useful. 11
  • 12. The Web of Linked Data• Analogy – a global database• Designed for – machines first, Humans later• Primary objects – things (or descriptions of things)• Links between – things• Semantics – explicit 12
  • 13. Linked Data: Technologies• Pre-requisite – URIs – HTTPs – RDF – (RDFS/OWL) 13
  • 14. Linked Data Technologies : URIs• Like URLs but not just for Web pages – For things (cars, people, places, organisations, coursework, etc. )• “A Uniform Resource Identifier (URI) provides a simple and extensible means for identifying a resource.” -- RFC 3986• Many different schemes – http://, ftp://, mailto:• Examples: http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf http://dbpedia.org/resource/University_of_Leeds 14
  • 15. HTTP• Data access mechanism between web browsers (client) and servers• HTTP messages consists of requests from client to servers and responses from servers to clients• HTTP request/response methods: GET, POST, etc. 15
  • 16. RDF• Data format to describe things and their interrelations• is based on triples• Subject, predicate, object• <The sky> <has the colour> <blue> 16
  • 17. RDF rdf:type dt:dhaval foaf:Person foaf:name Dhaval Thakker foaf:based_near dbpedia:Leeds From my profile in RDFPrefixesdt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#>rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/> 17dbpedia: <http://dbpedia.org/resource/>
  • 18. Data Merging with RDF rdf:type dt:dhaval foaf:Person foaf:name Dhaval Thakker foaf:based_near dbpedia:Leeds From my profile in RDF dbp-prop:population 751,500 dbpedia:Leeds dbp-prop: is part of dbpedia:West_Prefixes Yorkshiredt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#> From Dbpediardf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/>dbpedia: http://dbpedia.org/resource/ 18dbp-prop: <http://dbpedia.org/ontology/>
  • 19. Data Merging with RDF rdf:type dt:dhaval foaf:Person foaf:name Dhaval Thakker foaf:based_near dbpedia:Leeds From my profile in RDF dbp-prop:population 751,500 dbpedia:Leeds dbp-prop: is part of dbpedia:West_Prefixes Yorkshiredt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#> From Dbpediardf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/>dbpedia: http://dbpedia.org/resource/ 19dbp-prop: <http://dbpedia.org/ontology/>
  • 20. Linked Data Principles• Use URIs as names for things – anything, not just documents• Use HTTP URIs – globally unique names, distributed ownership – allows people to look up those names• Provide useful information in RDF – when someone looks up a URI• Include RDF links to other URIs – to enable discovery of related information Tim Berners-Lee 2007 20 http://www.w3.org/DesignIssues/LinkedData.html
  • 21. Linked Data Principles• Use URIs as names for things – anything, not just documents• Use HTTP URIs – globally unique names, distributed ownership – allows people to look up those names Tim Berners-Lee 2007 21 http://www.w3.org/DesignIssues/LinkedData.html
  • 22. Linked Data Principles• Use URIs as names for things – anything, not just documents• Use HTTP URIs – globally unique names, distributed ownership – allows people to look up those names• Provide useful information in RDF – when someone looks up a URI Tim Berners-Lee 2007 22 http://www.w3.org/DesignIssues/LinkedData.html
  • 23. Provide useful information in RDF rdf:type dt:me foaf:Person foaf:name Dhaval Thakker foaf:based_near dbpedia:Leeds From my profile in RDF http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#mePrefixesdt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#>rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/> 23dbpedia: <http://dbpedia.org/resource/>
  • 24. RDF is Data Model, Not Serialisation Format• RDF Serialisation Formats : RDF/XML, Turtle, N-Triples – RDF/XML <rdf:RDF xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns# xmlns:foaf=http://xmlns.com/foaf/0.1 /> <foaf:Person rdf:ID="me"> <foaf:name>Dhavalkumar Thakker</foaf:name> <foaf:title>Dr</foaf:title> <foaf:based_near rdf:resource="http://dbpedia.org/resource/Leeds"/> 24
  • 25. RDF is Data Model, Not Serialisation Format• RDF Serialisation Formats : RDF/XML, Turtle, N-Triples – Turtle @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix dt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf#> dt:me rdf:type foaf:Person ; foaf:name “Dhavalkumar Thakker" ; foaf:title “Dr" . 25
  • 26. RDF is Data Model, Not Serialisation Format• RDF Serialisation Formats : RDF/XML, Turtle, N-Triples – N-Triples < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf#me> <xmlns:foaf=http://xmlns.com/foaf/0.1#name> “Dhavalkumar Thakker”. < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf#me> < http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <xmlns:foaf=http://xmlns.com/foaf/0.1#Person>. 26
  • 27. Linked Data Principles• Use URIs as names for things – anything, not just documents• Use HTTP URIs – globally unique names, distributed ownership – allows people to look up those names• Provide useful information in RDF – when someone looks up a URI• Include RDF links to other URIs – to enable discovery of related information Tim Berners-Lee 2007 27 http://www.w3.org/DesignIssues/LinkedData.html
  • 28. Including Links to other Things: Relationship Links• Relationship Links point at related things in other data sources, for instance, other people, places or genes.• For example, relationship links enable people to point to background information about the place they live, or to bibliographic data about the publications they have written. 28
  • 29. Including Links to other Things: Relationship Links rdf:type dt:dhaval foaf:Person foaf:name Dhaval Thakker foaf:based_near dbpedia:Leeds From my profile in RDF dbp-prop:population 751,500 dbpedia:Leeds dbp-prop: is part of dbpedia:West_Prefixes Yorkshiredt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#> From Dbpediardf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/>dbpedia: http://dbpedia.org/resource/ 29dbp-prop: <http://dbpedia.org/ontology/>
  • 30. Including Links to other Things: Identity Links• Different URIs may refer to the same object <URI1> in one dataset is same as <URI2> defined somewhere else <http://dbpedia.org/resource/Kirkgate_Markets> <owl:sameAs> <http://rdf.freebase.com/ns/guid.9202a8c04000641f8000000000c5f680>• Such a need exists due to: – Different opinions. – Traceability. 30 – No central points of failure.
  • 31. Including Links to other Things: Vocabulary Links• Reusing existing Vocabularies to further specify yours<htttp://mydomain.co.uk/myvocab/enterprise#SmallMedium Enterprise>rdfs:subClassOf <http://dbpedia.org/ontology/Company>;rdfs:subClassOf<http://umbel.org/umbel/sc/Business> ;rdfs:subClassOf<http://rdf.freebase.com/ns/m/0qb7t>. 31
  • 32. Linked Data Principles: Summary Include Links: RDF serialisation• Use URIs as names for things Relationship, Vocab formats: – anything, not just documents ulary & Identity RDF/XML, N- Links Triples & Turtle• Use HTTP URIs – globally unique names, distributed ownership – allows people to look up those names• Provide useful information in RDF – when someone looks up a URI• Include RDF links to other URIs – to enable discovery of related information 32
  • 33. Finding Existing Datasets or Vocabularies• All of the scenarios about including links to other things assume some sort of knowledge of existing vocabularies/datasets• Where to Find such datasets?• How to Find such datasets? – Two steps: • Find datasets/vocabularies that contain certain Things or Concepts • Once found, how to inspect the coverage and suitability 33
  • 34. Where to Find: Web of Data• A significant number of individuals and organisations have adopted Linked Data as a way to publish their data• The result is a global data space we call the Web of Data• The Web of Data forms a giant global graph consisting of billions of RDF triples from numerous sources covering all sorts of topics 34
  • 35. Web of Datahttp://richard.cyganiak.de/2007/10/lod/ 35
  • 36. Statistics about Web of Data (2011) Number ofDomain Triples (Out-)Links % datasetsMedia 25 1,841,852,061 50,440,705 10.01 %Geographic 31 6,145,532,484 35,812,328 7.11 %Government 49 13,315,009,400 19,343,519 3.84 %Publications 87 2,950,720,693 139,925,218 27.76 %Cross-domain 41 4,184,635,715 63,183,065 12.54 %Life sciences 41 3,036,336,004 191,844,090 38.06 %User-generated 20 134,127,413 3,449,143 0.68 %content 295 31,634,213,770 503,998,829 More statistics from: http://www4.wiwiss.fu-berlin.de/lodcloud/state/ 36
  • 37. Step1: Finding existing datasets andvocabularies: publishing sites-> Data Hub Available from: http://datahub.io/ 37
  • 38. Step 1: Finding existing datasets andvocabularies: search engines-> Sindice Available from: http://sindice.com/ 38
  • 39. Step 1: Finding existing datasets andvocabularies: search engines-> Sindice 39
  • 40. Step 1: Finding existing datasets andvocabularies: search engines-> Sindice 40
  • 41. Step 1: Finding existing datasets andvocabularies: search engines-> FalconAvailable from: http://ws.nju.edu.cn/falcons/conceptsearch/index.jsp 41
  • 42. Finding existing datasets andvocabularies: search engines-> Watson Available from: http://kmi-web05.open.ac.uk/WatsonWUI/ 42
  • 43. Finding existing datasets andvocabularies: search engines-> Swoogle Available from: http://swoogle.umbc.edu/ 43
  • 44. Step 1: Finding existing datasets andvocabularies: search engines-> SWSE Available from: http://swse.deri.org/ 44
  • 45. Step 2: Once found, how to inspect further for coverage, suitability• Linked Data sources usually provides SPARQL endpoint for their dataset(s)• SPARQL endpoint is an end point to dataset(s) that can receive query, and return results• If you have used MySQL, you might be familiar with PhPMyAdmin – SPARQL endpoint are in similar in nature and its functionality 45
  • 46. Web of Datahttp://richard.cyganiak.de/2007/10/lod/ 46
  • 47. Dbpedia: Extracting Infoboxhttp://en.wikipedia.org/wiki/Calgaryhttp://dbpedia.org/resource/Calgarydbpedia:native_name Calgary”;dbpedia:altitude “1048”;dbpedia:population_city “988193”;dbpedia:population_metro “1079310”;mayor_name dbpedia:Dave_Bronconnier ;governing_body dbpedia:Calgary_City_Council;...
  • 48. Dbpedia: SPARQL Endpoint Web address: dbpedia.org/sparql
  • 49. SPARQL• Query Language for RDF – Based on RDF Data Model• Possible to write complex joins of disperate datasets• Implemented by all major RDF databasesSee more: http://www.w3.org/TR/rdf-sparql-query/ 49
  • 50. Structure of a SPARQL Query 50
  • 51. SELECT query: Find everything about Concept of “Person” as in Dbpedia#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>#result clauseSELECT *#dataset definitionFROM <http://dbpedia.org>#query patternWHERE {dbp-ont:Person ?p ?o. 51}
  • 52. SELECT query: Find everything about Concept of “Person” as in Dbpedia#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>#result clauseSELECT *#dataset definitionFROM <http://dbpedia.org>#query patternWHERE {dbp-ont:Person ?p ?o. 52}
  • 53. SELECT query: Find superclasses of Concept of “Person” as in Dbpedia#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>Prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>#result clauseSELECT ?o#dataset definitionFROM <http://dbpedia.org>#query patternWHERE {dbp-ont:Person rdfs:subClassOf ?o.} 53
  • 54. SELECT query: Find all persons in Dbpedia#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>#result clauseSELECT ?s#dataset definitionFROM <http://dbpedia.org>#query patternWHERE {?s rdf:type dbp-ont:Person .} 54
  • 55. SELECT query: Find specific types of persons in Dbpedia#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>#result clauseSELECT ?s#dataset definitionFROM <http://dbpedia.org> Some one#query pattern who isWHERE { Person &?s rdf:type dbp-ont:Person . Astronaut?s rdf:type dbp-ont:Astronaut. 55}
  • 56. SELECT query: Find specific types of persons in Dbpedia#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>#result clauseSELECT ?s#dataset definitionFROM <http://dbpedia.org> Some one#query pattern who isWHERE {?s rdf:type dbp-ont:Person . Person &?s rdf:type dbp-ont:Astronaut. Astronaut?s dbp-ont:status "Retired"@en. & Retired 56}
  • 57. SELECT query: Find 10 of this, LIMIT#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>#result clauseSELECT ?s#dataset definitionFROM <http://dbpedia.org>#query pattern Some oneWHERE { who is?s rdf:type dbp-ont:Person .?s rdf:type dbp-ont:Astronaut. Person &?s dbp-ont:status "Retired"@en. Astronaut} & Retired 57LIMIT 10
  • 58. SELECT query: Find 10 of this and order it by date: ORDER BY#prefix declarationprefix dbp-ont: <http://dbpedia.org/ontology/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>#result clauseSELECT * Some one#dataset definition who isFROM <http://dbpedia.org>#query pattern Person &WHERE { Astronaut?s rdf:type dbp-ont:Person . & Retired?s rdf:type dbp-ont:Astronaut. &?s dbp-ont:status "Retired"@en.?s dbp-ont:birthDate ?date youngest} ORDER BY ?date, firstLIMIT 10 58
  • 59. Mathematical operations &• Filtering results Find me all landlocked countries with a population greater than 15 million , with the highest population country firstPREFIX type: <http://dbpedia.org/class/yago/>PREFIX prop: <http://dbpedia.org/property/>SELECT ?country_name ?populationWHERE{ ?country a type:LandlockedCountries . ?country rdfs:label ?country_name . ?country prop:populationEstimate ?population .FILTER (?population > 15000000 && langMatches(lang(?country_name), "EN")) . }ORDER BY DESC(?population) 59
  • 60. ASK query: Is India a Landlocked country?• Is India a Landlocked country?• ASK query:PREFIX yago: <http://dbpedia.org/class/yago/>PREFIX prop: <http://dbpedia.org/property/>PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>ASK{ <http://dbpedia.org/resource/India> rdf:type yago:LandlockedCountries.} DO NOT HAVE TO SPECIFY “WHERE” Replace with Afghanistan 60
  • 61. Exercise: Write a SPARQL query• Write a SPARQL query to retrieve all the bands that are of genre rock bands from Republic of Ireland.Prefix dbpedia: <http://dbpedia.org/resource/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>Prefix dbp-onto: <http://dbpedia.org/ontology/>Use following classes or properties dbp-onto:Band, dbp-onto : genre. dbpedia:Rock_music, dbpedia:Republic_of_Ireland, dbp-ont:hometown 61
  • 62. Exercise: Write a SPARQL query• Write a SPARQL query to retrieve all the bands that are of genre rock bands from Republic of Ireland.Prefix dbpedia: <http://dbpedia.org/resource/>Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>Prefix dbp-onto: <http://dbpedia.org/ontology/>Select * where {?s rdf:type dbp-onto:Band.?s dbp-onto:genre dbpedia:Rock_music.?s dbp-onto:hometown dbpedia:Republic_of_Ireland 62}
  • 63. Summary: Finding existing datasets/vocabularies• Use of search engines to find a dataset• Use of SPARQL endpoints to inspect the dataset further• SPARQL queries – SELECT query for selecting a set of results to display – ASK query to ask a specific question about something – Variations in terms of LIMIT, ORDER BY 63
  • 64. Publishing Linked Data: Software Architecture Patterns• Follow linked data principles – They are good practice principles NOT norms or rules• The software architecture needs to support such way of publication – Existing architectures using structured or unstructured data – doing it from scratch – publishing linked data – different from when working with existing applications and infrastructure already in place 64
  • 65. Architecture scenarios 65
  • 66. Architecture scenarios 66
  • 67. Type of data Name Address Post code Author of• Structured data A ---- ------- Book B – Database tables – XML documents• Unstructured data – Textual documents • News stories, reports, textual descriptions – as textual files 67
  • 68. Architecture scenarios 68
  • 69. Query-able Structured Data to Linked Data• Example: A movie business that has movie database in a relational database• published relatively easily as Linked Data through the use of relational database to RDF wrappers.• Maps database schemas to RDF schemas• Wrappers – Virtuoso RDF Views – Triplify 69
  • 70. Architecture scenarios 70
  • 71. Static Structured Data to Linked Data• A UK government department that has performance data of each department in excel sheets• must undergo a conversion process that outputs static RDF files or loads converted data directly into an RDF store.• RDFizing tools – http://www.w3.org/wiki/ConverterToRdf – Tools to convert data from various format to 71 RDF
  • 72. RDF store• Also called “triple store” or “semantic repository”• They are engines similar to the DBMS- they allow for storage, querying, and management of structured data. Major differences: – they use ontologies as semantic schemata. This allows them to automatically reason about the data. – they work with flexible and generic physical data models (e.g. graphs). This allows them to easily interpret and adopt "on the fly" new ontologies or metadata schemata.• Available RDF stores: OWLIM, Allegrograph, 72 Virtuoso, Sesame, Jena TDB
  • 73. Architecture scenarios 73
  • 74. From Text Documents to Linked Data• Example: News publisher with a corpus of news stories produced in the last month• it is possible to pass these documents through a Linked Data entity extractor such as Open Calais(http://www.opencalais.com/), or DBpedia Spotlight(http://dbpedia- spotlight.github.com/demo/index.html) which annotate documents with the Linked Data URIs of entities referenced in the documents. 74
  • 75. From Text Documents to Linked Data• Publishing these annotations together with the documents – increases the discoverability of the documents – enables applications to use the referenced Linked Data sources as background knowledge to display complementary information on web pages – or to enhance information retrieval tasks, for instance, offer faceted browsing instead of simple full-text search.• Applications like this to be presented in next lecture(s) 75
  • 76. Summary• Linked Data is a way of publishing and interlinking structured data on the web• Linked Data principles to follow to create such data• How to find existing datasets: Web of Data• How to query existing datasets: SPARQL• Possible software architecture patterns 76
  • 77. Next Lecture• Consuming Linked Data – Linked Data Applications • What datasets they use from Web of Data • What software architecture they follow – Benefits • Integration – for organisations • Browsing and interaction – for users 77
  • 78. References• Tom Heath, An Introduction to Linked Data, Linked Data Tutorial, Austin, Texas, 2009.• Raimond et al., A skim-read introduction to linked data• Tom Heath, Christian Bizer: Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web, Morgan & Claypool Publishers 2011• Cambridge Semantics, SPARQL by example 78
  • 79. TED talk from Tim Berners Lee on Linked Data• http://www.ted.com/talks/tim_berners_lee_ on_the_next_web.html 79