The Internet Is Your New Database: An Introduction To The Semantic Web

Bendyconf 2014

Transcript

1. An Introduction to the Semantic Web Will Strinz! The Internet Is Your New Database
2. What’s this all about then? Today we have the World Wide Web
 
 
 
 
 
 
 
 

3. What’s this all about then? Today we have the World Wide Web
 
 
 
 
 
 
 
 

4. What’s this all about then? Its! Distributed! Accessible using all sorts of devices and software! Document Based! This is very flexible. But! Hard to search! Unstructured and context-less - hard to consume automatically! Easy to share, hard to compose and remix! Full of all sorts of ‘homebrew’ databases and ad-hoc schema
5. What’s this all about then? Database software is powerful but rarely interacted with directly! Often accessed through the web, but indirectly! Still ultimately siloed! Hard to compose and remix! What if the internet acted as one big distributed database?
6. Wat Do? Move from a web of documents to a web of data! Needs to be! Structured, but still flexible! Distributed! Accessible to machine and human alike
7. What is this Semantic Web Thing? Represents information using Subject Predicate Object
 
 Subject Predicate Object Will Strinz years old 24 Bendyworks a Company Will Strinz works at Bendyworks
8. What is this Semantic Web Thing? With URIs and typed literals
 
 
 
 
 
 Subject Predicate Object example.org/Will_Strinz example.org/years_old 24 example.org/Bendyworks example.org/is_a example.org/Company example.org/Will_Strinz example.org/works_for example.org/Bendyworks • Called Resource Description Format (RDF)
9. Example Lets start with a resource, or “thing”
10. Example Lets start with a resource, or “thing” We’ll call it http://example.org/Fresh, or ex:Fresh for short
11. Example What can we say about ex:Fresh? S P O ex:Fresh ex:enjoys “B-Ball” ex:Fresh ex:aunt ex:Vivian_Banks ex:aunt ex:enjoys ex:Vivian_Banks “B-Ball” ex:Fresh
12. Example ex:Vivian_Banks is also a resource ex:aunt “B-Ball” ex:Fresh ex:enjoys ex:Vivian_Banks S P O ex:Fresh ex:enjoys “B-Ball” ex:Fresh ex:aunt ex:Vivian_Banks
13. Example So now we can say things about her too! S P O ex:Fresh ex:enjoys “B-Ball” ex:Fresh ex:aunt ex:Vivian_Banks ex:Vivian_Banks ex:nickname “Aunt Viv” 1 ex:nickname “Aunt Viv” ex:aunt “B-Ball” ex:Fresh ex:enjoys ex:Vivian_Banks
14. Vocabularies We’ve been defining our own predicates and objects so far! Could add details about each predicate! Isn’t this a waste of time?! Yes! Use RDF Vocabularies! Define new namespaces, terms, and objects! ‘Imported’ simply by reference! Are described in RDF
15. FOAF Vocabulary “Friend Of A Friend”! Located at http://xmlns.com/foaf/0.1/ foaf:name a rdf:Property,! owl:DatatypeProperty;! rdfs:label "name";! rdfs:comment "A name for some thing.";! rdfs:domain owl:Thing;! rdfs:isDefinedBy foaf:;! rdfs:range rdfs:Literal;! rdfs:subPropertyOf rdfs:label;! sw_ns:term_status "testing" .!
16. Example S P O ex:Fresh foaf:based_near ex:West_Philedelphia ex:Fresh foaf:age 20 ex:Fresh foaf:name Will ‘The Fresh Prince’ Smith ex:aunt ex:enjoys “B-Ball” ex:Vivian_Banks ex:Fresh ex:nickname “Aunt Viv” foaf:based_near ex:West_Philadelphia! foaf:age 22 foaf:name “Will ‘The Fresh Prince’ Smith”
17. Example S P O ex:Fresh foaf:based_near ex:West_Philedelphia ex:Fresh foaf:age 20 ex:Fresh foaf:name Will ‘The Fresh Prince’ Smith ex:aunt ex:enjoys “B-Ball” ex:Vivian_Banks ex:Fresh ex:nickname “Aunt Viv” foaf:based_near ex:West_Philadelphia! foaf:age 22 foaf:name “Will ‘The Fresh Prince’ Smith”
18. Lets look at what we have Human readable! Simple and flexible! Rigid when necessary! Atomic statements! Dual representation! Ideally! Dereferencable! Structured, Machine understandable
19. Serialization Multiple formats! All of which are! Standardized! Interoperable! Information preserving! File or Triple Store
20. Serialization NTriples <http://example.orgfresh> <http://xmlns.com/foaf/0.1/name> "Will 'The Fresh Prince' Smith" .! <http://example.orgfresh> <http://example.orgenjoys> "B-Ball" .! <http://example.orgfresh> <http://xmlns.com/foaf/0.1/age> "22"^^<http://www.w3.org/2001/XMLSchema#integer> .! <http://example.orgfresh> <http://example.orgaunt> <http://example.orgVivian_Banks> .! <http://example.orgfresh> <http://xmlns.com/foaf/0.1/based_near> <http://example.orgWest_Philadelphia> .! <http://example.orgVivian_Banks> <http://example.orgnickname> "Aunt Viv" . Turtle @prefix ex: <http://example.org> .! @prefix foaf: <http://xmlns.com/foaf/0.1/> .! ! ex:fresh foaf:name "Will 'The Fresh Prince' Smith" ;! ex:enjoys "B-Ball" ;! foaf:age 22 ;! ex:aunt ex:Vivian_Banks ;! foaf:based_near ex:West_Philadelphia .! ! ex:Vivian_Banks ex:nickname "Aunt Viv" .
21. Serialization JSON-LD {! "@context": { "ex": "http://example.org",! ! "foaf": "http://xmlns.com/foaf/0.1/" },! "@graph": [ { "@id": "ex:Vivian_Banks",! ! "ex:nickname": "Aunt Viv" },! ! { "@id": "ex:fresh",! ! "ex:aunt": { "@id": "ex:Vivian_Banks" },! ! "ex:enjoys": "B-Ball",! ! "foaf:age": { "@value": "22",! !! "@type": "http://www.w3.org/2001/XMLSchema#integer" },! ! "foaf:based_near": { "@id": "ex:West_Philadelphia" },! ! "foaf:name": "Will 'The Fresh Prince' Smith" } ]! }
22. Serialization RDF/XML <?xml version='1.0' encoding='utf-8' ?>! <rdf:RDF xmlns:ex='http://example.org' xmlns:foaf='http://xmlns.com/foaf/0.1/' xmlns:rdf='http://www.w3.org/ 1999/02/22-rdf-syntax-ns#' xmlns:xsd='http://www.w3.org/2001/XMLSchema#'>! <rdf:Description rdf:about='http://example.orgVivian_Banks'>! <ex:nickname>Aunt Viv</ex:nickname>! </rdf:Description>! <rdf:Description rdf:about='http://example.orgfresh'>! <ex:aunt rdf:resource='http://example.orgVivian_Banks' />! <ex:enjoys>B-Ball</ex:enjoys>! <foaf:age rdf:datatype='http://www.w3.org/2001/XMLSchema#integer'>22</foaf:age>! <foaf:based_near rdf:resource='http://example.orgWest_Philadelphia' />! <foaf:name>Will 'The Fresh Prince' Smith</foaf:name>! </rdf:Description>! </rdf:RDF>
23. Serialization Other Formats! TRiG/TRiX! RDFa! N3! NQuads! Mappings onto SQLite, Mongo, etc
24. Querying What new language do I have to learn just to query! SPARQL!! But stick with me, its not so bad! Syntax similar to SQL, but joins are free!! Much more consistent across endpoints and triple stores
25. Querying How old is Will ‘The Fresh Prince’ Smith?
 
 PREFIX foaf: <http://xmlns.com/foaf/0.1/>
 PREFIX ex: <http://example.org>
 
 SELECT (?age) WHERE {
 ex:Fresh foaf:age ?age
 }
 
 => 22
26. Querying What is Will ‘The Fresh Prince’ Smith’s Aunt’s nickname?
 
 SELECT (?nick) WHERE {
 ex:Fresh ex:aunt ?aunt .
 ?aunt ex:nickname ?nick .
 }
 => “Aunt Viv”
27. Querying What do we know about Will ‘The Fresh Prince’ Smith?
 
 SELECT (?prop) WHERE {
 ex:Fresh ?prop ?value .
 }! ! => foaf:name, ex:enjoys, foaf:age, ex:aunt, foaf:based_near
28. Querying • Update, Insert, and Delete! • Logical / Regex filters! • Subqueries! • Order and Offset! • Aggregates Many other features in SPARQL • Data Types! • Construct, Describe, and Ask modes! • Order and Offset! • Property Paths! • Math functions
29. Getting Connected So, RDF has some cool features
 
 
 
 
 
 
 
 • Flexible yet structured! • Atomic! • Graph Based! • W3C Backed • Human Friendly! • Queryable! • Serializable! • Extensible And I can say things about Will Smith with it But how different is that really from any other database?! Connections!
30. Getting Connected Remember our ex:Fresh URI?! http://example.org/Fresh! Its not dereferencable.! What if instead we used! http://dbpedia.org/resource/Will_Smith_(character)
31. Getting Connected Suddenly we have more information dbpedia:Will_Smith_(character) a yago:FictionalCharacter,! dbpedia-owl:FictionalCharacter,! yago:FictionalVersionsOfRealPeople,! yago:ImaginaryBeing109483738,! dbpedia-owl:Person,! dbpedia-owl:Agent,! yago:SitcomCharacters,! owl:Thing,! foaf:Person;! rdfs:label "Will Smith (character)”@en;
32. Getting Connected A lot more information dbpedia-owl:abstract "William \"Will\" Smith (born July 3, 1973) is a fictional character in the NBC television series, The Fresh Prince of Bel-Air."@en;! dbpedia-owl:birthDate "1973-07-02+02:00"^^xsd:date;! dbpedia-owl:portrayer dbp:Will_Smith;! dbpedia-owl:series dbp:The_Fresh_Prince_of_Bel-Air;! dbpedia-owl:wikiPageExternalLink <http://www.imdb.com/character/ch0020905/>;! dbpprop:born "1973-07-02+02:00"^^xsd:date;! dbpprop:family "Janice Smith"@en,! "Hilary Banks"@en,! "Carlton Banks"@en,! "Vy Smith-Wilkes"@en,! "Lisa Wilkes"@en,! "Lou Smith"@en,! "Ashley Banks"@en,! "Helen Smith"@en,! "Fred Wilkes"@en,! "Phillip Banks"@en,! "Vivian Banks"@en;
33. Getting Connected ! dbpprop:first "\"The Fresh Prince Project\""@en;! dbpprop:hasPhotoCollection informatik:Will_Smith_(character);! dbpprop:name "Will Smith"@en;! dbpprop:nicknames "Master William, Prince, Fresh Prince, Will"@en;! dbpprop:portrayer dbp:Will_Smith;! dbpprop:series dbp:The_Fresh_Prince_of_Bel-Air;! dbpprop:wordnet_type wn:synset-character-noun-4;! dcterms:subject category:Fictional_characters_introduced_in_1990,! category:Fictional_African-American_people,! category:Fictional_versions_of_real_people,! dbpcategory:Fictional_characters_from_Philadelphia,_Pennsylvania,! category:Sitcom_characters;! rdfs:comment "William \"Will\" Smith (born July 3, 1973) is a fictional character in the NBC television series, The Fresh Prince of Bel-Air."@en;! owl:sameAs <http://rdf.freebase.com/ns/m.0417_vv>,! <http://yago-knowledge.org/resource/Will_Smith_(character)>,! <http://dbpedia.org/resource/Will_Smith_(character)>;! foaf:isPrimaryTopicOf <http://en.wikipedia.org/wiki/Will_Smith_(character)>;! foaf:name "Will Smith"@en .
34. Getting Connected Human AND Machine Readable
35. Getting Connected DBPedia has a SPARQL Endpoint! Lots of fun queries
 
 
 
 
 SELECT * WHERE {
 ?episode dc:subject dbpcategory:The_Simpsons_%28season_14%29_episodes . 
 ?episode dbpedia2:blackboard ?chalkboard_gag .
 }
36. Getting Connected Larger goal of connecting the whole web
 
 I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day- to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The "intelligent agents" people have touted for ages will finally materialize. — Sir Tim Berners-Lee (1999)
37. http://dgallery.s3.amazonaws.com/lod-cloud_colored.png
38. Going Further Templating with RDFa or XSLT! Ontologies / OWL! Reasoning! Libraries! Ruby-RDF! Spira! Publisci and Publisci Server! Tools and interfaces - Build them!
39. Disclaimers Relatively young! Less engineering time! SPARQL changing and not fully implemented in all triple stores! Flexibility has its downsides; Garbage in Garbage out! No agreed upon method for schema constraints
40. End Thanks Bendyconf Attendees and Organizers!
 Questions?