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![• BBC Music is underpinned by the
Musicbrainz music database and
Wikipedia.
• “BBC Music takes the approach that the
Web itself is its content management
system. [BBC] editors directly
contribute to Musicbrainz and
Wikipedia.”](https://image.slidesharecdn.com/damasffinalprint-127619631893-phpapp01/85/Introduction-tothe-Semantic-Web-and-Linked-Data-134-320.jpg)
Uploaded byEric Franzon
Introduction tothe Semantic Web and Linked Data
The document introduces the concept of the Semantic Web and Linked Data, emphasizing their roles in connecting and meaningfully organizing information on the internet. It discusses various semantic technologies, their functionality, and uses, as well as the importance of using shared vocabularies and structured data formats like RDF and SPARQL. Additionally, it highlights real-world applications, challenges, and the growing need for data scientists in the context of vast data production.
Introduction tothe Semantic Web and Linked Data
- 1. Introduction to the SemanticWeb and Linking Data Eric Axel Franzon Vice President Semantic Universe/ Wilshire Conferences
- 2. About Me • Professional • Wilshire Conferences • Semantic Universe • W3C • Guidewire Group • Coach / Consultant / Trainer • Geek
- 3. Today we willtalk about: • Semantic Technologies • Semantic Web & Web 3.0 • Linked Data – Linked Open Data – Linked Enterprise Data • Use cases • That harmonica on the first slide
- 4.
- 5.
- 6. Semantic Web =Web 3.0 = Web of Data
- 7.
- 8. What is theWeb of Data Not? • A software package • Something that will ever “be complete” • A replacement for the current Web • A pipe dream • A silver bullet
- 9. It’s also not… •HAL 9000
- 10. It’s also not… •Skynet
- 11. What is theWeb of Data? • A Web-scale architecture • A metadata technology • A layer of meaning on the existing Web • In use TODAY!
- 12.
- 14. Q: What doesLinked Data have to do with the Semantic Web?
- 15. Web 1.0 –Linking Documents
- 16.
- 17. Web 1.0 “I see:characters + formatting + images” --my Computer
- 18. Web 1.0 –Linking Documents Web 2.0 – Linking People
- 19.
- 20. Web 2.0 “I see:characters + formatting + images” --my Computer
- 21. Web 1.0 –Linking Documents Web 2.0 – Linking People Web 3.0 – Linking Data
- 22. Web 3.0 –Linking Data Title Publisher Format Author Price Cover
- 23. Web 3.0 –Linking Data Title Publisher Format Author “I see: things Price Cover + relationships. This information is about a book.”
- 25. Semantic Technologies Semantic Linked Web Open Data
- 26. Linking Open DataProject May, 2007
- 27.
- 30. Data from thesetrusted sources is available for you to use in your applications TODAY. Data you can LINK to. And not just data…
- 31. Semantic Data thatis not only machine READABLE. It is machine UNDERSTANDABLE!
- 32.
- 33. Disambiguation mole, n.
- 35.
- 36. Metadata Doctorow’s Criticisms LOD/LEDResponse “People lie” Allow users to choose a social trust model Automate where possible and encourage “People are lazy” authoring where needed Automate where possible, check where “People are stupid” possible “Mission Impossible: know thyself” Allow multiple sources of metadata “Schemas aren’t neutral” Allow multiple schemas “Metrics influence results” Allow multiple metrics “There’s more than one way to describe Allow multiple descriptions something”
- 37.
- 38. How does LOD/LEDwork? 1. By uniquely identifying THINGS 2. By uniquely identifying RELATIONSHIPS 3. By using TRIPLES
- 39. How does LOD/LEDwork? 1. By uniquely identifying THINGS So, what’s a THING?
- 40. A THING isanything that can be uniquely identified by a URI or a literal (string) Me http://twitter.com/ericaxel My postal code http://www.city-data.com/zips/90043.html The White House Lat: 38.89859 Long: -77.035971 L.A. County’s sales tax rate 9.750 % http://ericfranzon.com/operator.jpg
- 41. This is acollection of THINGS: t_people Name City State Post code David Fredericksburg VA 22408 Eric Culver City CA 90230
- 42. Trees and Tables t_people Name City State Post code David Fredericksburg VA 22408 Eric Culver City CA 90230 people David Eric City City State Post State Post code code Fredericksburg VA 22408 Culver City CA 90230
- 43. Trees and Tables– Problem 1 Name City t_people State Post code flag Adding partial David Fredericksburg VA 22408 1 data to Eric Culver City CA 90230 tables leads to people sparseness flag 1 David Eric City City State Post State Post code code Fredericksburg VA 22408 Culver City CA 90230
- 44. Trees and Tables– Problem 2 t_people Common data Name City State Post code leads to (lots!) David Culver City CA 90230 of duplication Eric Culver City CA 90230 people David Eric City City State Post State Post code code Culver City CA 90230 Culver City CA 90230
- 45. Graphs people flag 1 David Eric City City Post Post code code Culver City State State CA 90230
- 46. How does LOD/LEDwork? 1. By uniquely identifying THINGS 2. By uniquely identifying RELATIONSHIPS Who’s your daddy?
- 47. Is Father of mailto:ericaxel@yahoo.com <owl:ObjectProperty rdf:ID="isFather"> <rdfs:domain rdf:resource="#Person"/> <rdfs:range rdf:resource="#Person"/> </owl:ObjectProperty>
- 48. 1. By uniquelyidentifying THINGS 2. By uniquely identifying RELATIONSHIPS 3. By using TRIPLES What’s a triple?
- 49. Triples? It’s Elementary!(School) book has title. Relationship Predicate That is a Triple!
- 50. Triples? It’s Elementary! “This book has a title.” “Eric wrote this Web page.” “This article is about moles.” “I like blues.” “I like B.L.U.E.S.” “This image can be used non-commercially.” “My email address is ericaxel@yahoo.com.”
- 51. Triples Book Has Title “Title” Created Objects Subjects Eric Webpage Has License CC Non- Image Commercial Predicates
- 52. Author Title Book ISBN Publisher
- 53. The Trouble withTriples
- 54.
- 55. Our Data areMultiplying. Review of the Review
- 56. Trends in datagrowth • Vast amounts of digital data being produced daily. –Wal-Mart produces 1 million transactions every hour. DBs estimated at > 2.5 petabytes • US National Archives creating > 10 million digital assets annually
- 57. Data Inflation • Megabyte (MB) = 220 • Gigabyte (GB) = 230 • Terabyte (TB) = 240 • Petabyte (PB) = 250 or 1000TB • Exabyte (EB) = 260 or 1,000PB 70 • Zettabyte (ZB) = 2 or 1,000EB 80 • Yottabyte (YB) = 2 or 1,000ZB
- 58. Acceleration –Decoding human genomeinvolves analyzing 3 billion base pairs • what took 10 years to process in 2003, takes a week today
- 59. A brand newprofessional has emerged .... The data scientist who combines the , skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. - The Economist, “Data, data everywhere”, Feb 27th 2010
- 60. When we comeback…
- 61. S – T– R – E – T – C - H Brea k!
- 62. Linked Data is likea harmonica • It’s easy to play
- 63. Facebook • Unique Visitors*:540,000,000 • Page Views: 570,000,000,000 * Per month Source: Google - The 1000 most-visited sites on the web
- 64.
- 65.
- 66.
- 67.
- 68.
- 69.
- 70. semantictweet.com Can create fourFOAF files: • Friends (who I follow) • Followers • All • Just Me
- 71. Linked Data is like a harmonica • It’s easy to play • It’s a “real” instrument
- 72. The Technologies ofRDBMS • Data • Schemas • Query Language
- 73. RDBMS Data t_people Name City State Post code David Fredericksburg VA 22408 Eric Culver City CA 90230
- 74.
- 75. RDBMS Query Language:SQL SELECT isbn, title, price, price * 0.06 AS sales_tax FROM Book WHERE price > 100.00 ORDER BY title;
- 76. The Technologies ofLOD/LED • Data • Schemas • Query Language
- 77.
- 78. RDF Triples Subject Predicate Object http://plushbeautybar.com dc: creator http://www.ericax el.com/foaf.rdf http://www.geonames.org/ dc: location N 34° 1' 16'' maps/google_34.021_- W 118° 23' 47'' 118.396.html http://twitter.com/ericaxel foaf: knows “Brian Sletten”
- 79. RDF Triple Components Subject Predicate Object http://plushbeautybar.com dc: creator http://www.ericax el.com/foaf.rdf http://www.geonames.org/ dc: location N 34° 1' 16'' maps/google_34.021_- W 118° 23' 47'' 118.396.html http://twitter.com/ericaxel foaf: knows “Brian Sletten” http://twitter.com/bsletten URI URI URI or String Literal
- 80. “RDF is goodfor distributing data across the Web and pretending it’s in one place.” -Dean Allemang, TopQuadrant
- 81. Just so youknow… There are many ways of representing RDF: • RDF/XML • N-Triples • N3 • Turtle • JSON • RDFa Each serialization has pros and cons, but they all are used to connect THINGS and RELATIONSHIPS into TRIPLES
- 82. The Schemata Linked Dataschemas consist of: Your RDF relationships (predicates) + Relationship descriptions
- 83. LOD/LED Schemata id First Name Last Name Schema Relationship 1 Tony Shaw Data description hasSurname owl:sameAs Initial Schema hasFirstName hasLastName hasID Tony 1 Shaw
- 84. Choosing Relationships • Reusepopular vocabularies –FOAF (Friend-of-a-friend) –Dublin Core (library/publisher metadata) –SIOC (Semantically-Interlinked Online Communities) • ...or make up your own!
- 85. RDF Triples Subject Predicate Object http://plushbeautybar.com dc: creator http://www.ericax el.com/foaf.rdf http://www.geonames.org/ dc: location N 34° 1' 16'' maps/google_34.021_- W 118° 23' 47'' 118.396.html http://twitter.com/ericaxel foaf: knows “David Wood”
- 86. Relationship Descriptions 1. ResourceDescription Framework Schema (RDFS): Simple, hierarchical classes 2. Simple Knowledge Organization System (SKOS): Port taxonomies to the Semantic Web 3. Web Ontology Language (OWL): Complex logical relationships
- 87. Combine vocabularies anddescriptions
- 88. LOD/LED Schemata • Putas much work into creating your LED schema as you put into creating your relational schemas • ... maybe even a bit more (due to links between your data and others’).
- 89. New York Times-SKOS
- 90. New York Times-SKOS
- 91. New York Times-SKOS SKOS STUFF
- 92. The query language SPARQL SPARQL Protocol And RDF Query Language
- 93. SPARQL Example #1 FOAF(some people that Eric Franzon knows) PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name FROM <http://ericaxel.com/eric.rdf> WHERE { ?knower foaf:knows ?known . ?known foaf:name ?name . }
- 94.
- 95. Example #1 -Results
- 96. SPARQL Example #2 Querying two FOAF Profiles PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?name FROM NAMED <http://ericaxel.com/eric.rdf> FROM NAMED <http://zepheira.com/team/dave/dave.rdf> WHERE { GRAPH <http://ericaxel.com/eric.rdf> { ?x rdf:type foaf:Person . ?x foaf:name ?name . } . GRAPH <http://zepheira.com/team/dave/dave.rdf> { ?y rdf:type foaf:Person . ?y foaf:name ?name . } . }
- 97. Example #2 -Results
- 98. SPARQL Example #3 Bart Simpson's chalkboard gags (DBPedia) SELECT ?episode,?chalkboard_gag WHERE { ?episode skos:subject ?season . ?season rdfs:label ?season_title . ?episode dbpedia2:blackboard ?chalkboard_gag . FILTER (regex(?season_title, "The Simpsons episodes, season")) . } ORDER BY ?season
- 99. Example #3 -Results
- 100.
- 101. Are *real* companiesusing Linked Data?
- 102. Easy to play;takes work to master.
- 103.
- 104. E-Commerce Avocabulary to describe products, services, and other e-commerce terms.
- 105. Who is usingGoodRelations? 1100+ Best Buy stores
- 106. Phase 2 ~640,000 “next-gen”product detail pages
- 107. 21 Open BoxProducts listed at this store!
- 109. Who is usingGoodRelations?
- 113. With RDFa +GoodRelations, but no additional SEO work, PlushBeautyBar.com was indexed by Google within one week.
- 114. Semantic (Web) Technologies Linked RDBMS Enterprise Semantic Linked Data CRM Web Open Data Calendars
- 115. MIXING private andpublic data? Absolutely! And it’s really useful to do so!
- 116.
- 117. Public + PrivateData: iConcertCal
- 118. Public + PrivateData: iConcertCal
- 119.
- 120. Siri.com Siri is aVirtual Assistant. I ask it to do things for me. It does, by mixing data, by disambiguating, and by reasoning.
- 121. Siri.com Siri is aVirtual Assistant. I ask it to do things for me. It does, by mixing data, by disambiguating, and by reasoning.
- 122. Siri.com Siri is aVirtual Assistant. I ask it to do things for me. It does, by mixing data, by disambiguating, and by reasoning.
- 123. Siri.com Siri is aVirtual Assistant. I ask it to do things for me. It does, by mixing data, by disambiguating, and by reasoning.
- 124. Siri.com Siri is aVirtual Assistant. I ask it to do things for me. It does, by mixing data, by disambiguating, and by reasoning.
- 125. Siri.com Siri is aVirtual Assistant. I ask it to do things for me. It does, by mixing data, by disambiguating, and by reasoning.
- 126.
- 127. • Largest broadcasting corp. in the world • 8 national TV channels • 10 national radio stations • 40 local radio stations • An extensive website, bbc.co.uk
- 128. • Broadcasts 1,000-1,500programs per day. • Publishes information in several formats: audio, video, textual. • Needed to relate information across media for both users and third-party developers
- 129. • Approach: Createa Web presence for each • Broadcast • Artist • Species (and other biological ranks), habitat and adaptation –that the BBC has an interest in.
- 133. "Creating web identifiersfor every item the BBC has an interest in, and considering those as aggregations of BBC content about that item, allows us to enable very rich cross-domain user journeys." -- Yves Raimond
- 134. • BBC Musicis underpinned by the Musicbrainz music database and Wikipedia. • “BBC Music takes the approach that the Web itself is its content management system. [BBC] editors directly contribute to Musicbrainz and Wikipedia.”
- 136. BBC • Wildlife Finderlinks existing LOD data with BBC content to make pages about each species, habitat and adaptation: • Wildlife programmes (clips and episodes) are identified by tagging the clip or episode with the appropriate dbpedia URI.
- 138. "The RDF representationsof these web identifiers allow developers to use our data to build applications." -- Yves Raimond
- 139. A few finalthoughts
- 140. A little bitcan be very powerful!
- 142. RDFs RDF RDFa OWL triple Web 3.0 = Semantic Web SPARQL Linked Data SKOS
- 143. RDFs Dublin Core OWL-DL OWL-Full NLP RDF RDFa triplestore PURLs OWL OWL2 triple OWL-lite vocabulary microdata ontology folksonomy subject predicate object entity extraction Web 3.0 = Semantic Web SPARQL microformats REST GRDDL taxonomy URI Artificial Intelligence cloud computing open world reasoning LOD LED reasoning engine Linked Data data portability SKOS
- 144. Further Reading …and moreto come!
- 145. THANK YOU! Questions? Operators are standing by. EricAxel@yahoo.com
- 146. Semantic Technology Conference www.Semantic-Conference.com June 21-25, 2010 Semantic Universe Free Informational Resource www.SemanticUniverse.com
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