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OSM Lecture ( 14:45 -16:15) Takahira Yamaguchi OSM Exercise (16:30-18:00) Susumu Tamagawa

OSM Lecture ( 14:45 -16:15) Takahira Yamaguchi OSM Exercise (16:30-18:00) Susumu Tamagawa. TBL 1 st Proposal Information Management : A Proposal (1989). Links have the following types: depends on is part of made refers to uses is an example of Users should do data analysis by WWW:

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OSM Lecture ( 14:45 -16:15) Takahira Yamaguchi OSM Exercise (16:30-18:00) Susumu Tamagawa

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  1. OSM Lecture (14:45-16:15)Takahira YamaguchiOSM Exercise (16:30-18:00)Susumu Tamagawa

  2. TBL 1st Proposal InformationManagement: A Proposal(1989) • Links have the following types: • depends on • is part of • made • refers to • uses • is an example of • Users should do data analysis by WWW: • Automatic analysis such as comparison • Automatic generation of mailing lists • Deriving real organization structure

  3. TBL 2nd Proposal (2001)Tim Berners Lee, James Hendler, OraLassila: The Semantic Web, http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21&sc=I10032 • Semantic Web • Make Web real Hypertext • Data(instance) should have types (class) • Links(property) should have types • Links = relationship between resources • Data Analysis • SW can make inference “The Semantic Web will globalize KR, just as the WWW globalized hypertext

  4. Big Picture for Semantic Web (2001) http://www.semanticweb.org/about.html#bigpicture

  5. Intelligence • Machine Intelligence →Knowledge Representation (1980- : Expert Systems 1990-: Knowledge Modeling -> Ontologies) • Web Intelligence →(2000-: Semantic Web)

  6. (Domain Expert) Expert System Overview Major Components Knowledge Base Inference Engine Working Space ＥＦ ＩＥ ＫＢ ＤＩ User ＫＡ ＷＳ Other Components Knowledge Acquisition Explanation Facility Dialog Interface

  7. Expert Systems from KMHow Organizations Manage What They Know,by Thomas H. Davenport & Laurence Prusak (1998) • Technical systems in narrow domain knowledge • not been supplanted as knowledge providers • difficult to maintain or add knowledge (rules) (Authorizer’s Assistant ES for credit authorization → knowledge is stable and so still used in AMEXXCON →knowledge changes and so DEC stopped using it) • Why so hard to maintain rules?→ other experts besides developer do not understand what rule primitives mean.So they should be specified.→ontologies (in the filed of knowledge engineering)

  8. KE 90-: an explicit specification of conceptualization by Tom Gruber 93 Ontolingua General OntologyCYC, WordNet, EDR… Ontology Development Methodology Ontoligies in KE and Semantic Web • Semantic Web • 95-97: XML as arbitrary structures • 97-98: RDF • 98-99: RDFS • 2001: DAML+OIL 2nd proposal by TBL • 2004.2.10: OWL • 2009.10 OWL2 • 2010.7 RIF

  9. Semantic Web Stack 2001->2006

  10. Collective Intelligence Wikipedia Folksonomy Linked Open Data (LOD) Open Data Government UK USA Media Science Science Commons Government MusicBrainz Music Flickr Pictures Life Science YAHOO! Scraping Annotation SearchMonkey GRDDL RDFa Dapper HTML5 microdata Google Piggy Bank Rich Snippets microformats Web Pages 2010 2001 2006

  11. See Movie on Semantic Web Introduction (4 minutes)

  12. RDF(Resource Description Framework)

  13. RDF Basic (Data) Model（１） Resource：information resource identifiable by URI Property: relationship between resources or resource’s attribute Statement = (Subject, Predicate, Object) (Tuple) ① (Resource, Property,(relationship), Resource) ② (Resource, Property(attribute) , Value) URI URI URI

  14. Takahira Yamaguchi dc: creator http://www.yamaguti.comp.ae.keio.ac.jp/ Property Resource Literal Statement RDF Basic Model（2） • RDF Data Model -> Arced Graph with Lables • Subject, Object: NodePredicate: Arc (Property) • Resource: Ellipse (Subject, Object )Literal: Rectangle (Object )

  15. Ueda Tama breed male sex Ueda Tama breed color white RDF Structured Model (1) Putting together two or more statementsSome resource is object in statement 1 and subject in statement 2.

  16. RDF Structured Model (2)Blank Node http://www.bb2.com’s author is Ryou Imai. His mail address is webmaster@imai.com. Ryou Imai Name author http://www.bb2.com webmaster@imai.com mail address Blank Node To put together resources Outside web sites cannot see it.

  17. RDF Syntax (1) RDF Syntax XML <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"> <rdf:Descriptionrdf:about="http://kanzaki.com"> <dc:creator>Takahira Yamaguchi</dc:creator> </rdf:Description> </rdf:RDF> RDF Model RDF Syntax Notation3 dc:creator @prefix dc: <http://purl.org/dc/elements/1.1/> . @prefix rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#> . <http://kanzaki.com> dc:creator“Takahira Yamaguchi" . http://yamaguchi.com Takahira Yamaguchi RDF Syntax N-Triple <http://kanzaki.com> <http://purl.org/dc/elements/1.1/creator> “Takahira Yamaguchi" .

  18. RDF ＸＭＬ Syntax (1) Basic Model dc:creator http://yamaguchi.com Takahira Yamaguchi rdf: RDF Model & Syntax name space Description element：statement About attribute：Subject resource’s URI Description element ：property(dc:creator)＋value（Takahira Yamaguchi） <rdf:Descriptionrdf:about=“http://yamaguchi.com"> <dc:creator>Takahira Yamaguchi</dc:creator> </rdf:Description> • Shorter Version <rdf:Description rdf:about=“http://yamaguchi.com“dc:creator=“Takahira Yamaguchi”/>

  19. RDF ＸＭＬ Syntax (2) Structured Model ex:webmaster http://www.ohmsha.co.jp/ Someone’s name dc:publisher urn: ISBN-13: 978-4274203480 dc:creator Takahira Yamaguchi <rdf:RDF...> <rdf:Descriptionrdf:about=“urn: ISBN-13:978-4274203480 "> <dc:creator>Takahira Yamaguchi</dc:creator> <dc:publisher> <rdf:Descriptionrdf:about=" http://www.ohmsha.co.jp "> <ex:webmaster>Someone’s name</ex:webmaster> </rdf:Description> </dc:publisher> </rdf:Description> </rdf:RDF>

  20. RDFSRDF Scheme

  21. male sex Ueda Tama breed color white RDFS What does ‘breed’ mean? What does ‘sex’ mean? What does ‘color’ mean? What is Ueda? What class? What is Tama? What class? If software does not make sense of the above names (symbols), there is no way to enable semantic-based intelligent processing. -> Ontologies (RDFS+OWL)

  22. RDFS vocabulary • RDFS 4 Basic Classes • rdfs:Resource: • rdfs:Class： • rdf:Property： • rdfs:Literal： • RDFS 7 Basic Properties • rdf:typeclass-instance • rdfs:subClassOfsuperclass-subclass • rdfs:subPropertyOfsuper property-subproperty • rdfs: domain (what subject should be) • rdfs: range (what object should be) rdfs:label rdfs:comment rdf: -> http://www.w3.org/1999/02/22-rdf-syntax-ns# prefix rdfs: -> http://www.w3.org/2000/01/rdf-schema# prefix

  23. How to make class <rdf:Descriptionrdf:ID=“animal"> <rdf:typerdf:resource="http://www.w3.org/2000/01/rdf-schema #Class"/> or <rdf:typerdf:resoucre=“&rdfs;Class”/> <rdfs:subClassOfrdf:resource=“#living thing"/> </rdf:Description> <rdfs:Classrdf:ID=“dog"> <rdfs:subClassOfrdf:resource=“#animal"/> </rdfs:Class> living thing is-a animal is-a dog

  24. How to make “person” class <rdf:Description rdf:ID=“Person”> <rdf:type rdf:resource=“&rdfs;Class”/> <rdfs:subClassof rdf:resource=“rdfs;Resource”/> <rdfs:Class ID=“Person”> <rdfs:subClassof rdf:resource=“rdfs;Resource”/> </rdfs:Class>

  25. How to make property • rdfs:subPropertyOfsuper property-subproperty • rdfs: domain (what subject should be) • rdfs: range (what object should be) <rdf:Propertyrdf:ID=“breed"> <rdfs:subPropertyOfrdf:resource=“．．．"/> <rdfs:domainrdf:resource=“#person"/> <rdfs:rangerdf:resource=“#cat"/> </rdf:Property>

  26. How to make instance rdf:typeclass-instance <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" > <rdf:Descriptionrdf:about=“#Tama"> <rdf:typerdf:resource="#cat"/> </rdf:Description> <rdf:Descriptionrdf:about=“#Ueda"> <rdf:typerdf:resource=“#person"/> <breedrdf:resource=“#tama"/> </rdf:Description> </rdf:RDF> person cat rdf:type rdf:type breed Ueda Tama

  27. RDF RDFS Syntax Syntax Resource Literal <rdf:Description about=“William”> <hasFather>Charles<hasFather/> <rdf:type resource=“Person”/> </rdf:Description> <rdf:Class rdf:about=“Person”> <rdfs:subClassOf resource=“&rdfs;Resource”/> </rdf:Class> <rdf:Property rdf:about=“hasFather”> </rdf:Property> Property Model Model rdf:type rdfs:Class Person rdf:type Person rdfs:subClassOf object(Resource) William hasFather rdfs:Resource (Class) Charles rdf:type predicate （Property） subject （Resource） rdf:Property hasFather object(Literal) (Property) statement RDF/RDFS

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