INTRODUCTION TO Elena Simperl, University of Southampton, UK THE - - PowerPoint PPT Presentation

introduction to
SMART_READER_LITE
LIVE PREVIEW

INTRODUCTION TO Elena Simperl, University of Southampton, UK THE - - PowerPoint PPT Presentation

INTRODUCTION TO Elena Simperl, University of Southampton, UK THE SEMANTIC WEB e.simp mperl@sot soton.ac.uk @esimperl rl THE BEGINNINGS The Semantic Web is no not a a sep separate We Web but an extens nsion n of the he c current


slide-1
SLIDE 1

INTRODUCTION TO THE SEMANTIC WEB

Elena Simperl, University of Southampton, UK e.simp mperl@sot soton.ac.uk @esimperl rl

slide-2
SLIDE 2

THE BEGINNINGS

“The Semantic Web is no not a a sep separate We Web but an extens nsion n of the he c current

  • n
  • ne, in which information is given

we well-de defined m meaning ng, better enabl bling ng com

  • mpu

puters a and nd p peopl ple to to wo work rk i in cooperat ation

  • n”

[Berners-Lee, Hendler & Lassila, 2001]

slide-3
SLIDE 3

NOT A SEPARATE WEB

Dece centr tralized ed i informati tion s space ce (for people) Consisting of documents and other Web resources

 Uniquely identified  Connected to each other via hyperlinks  Accessed via the Internet  Created and used by different parties

Technologies

 URIs  HTML  HTTP

http://info.cern.ch/hypertext/WWW/TheProject.ht ml

slide-4
SLIDE 4

BUT AN EXTENSION OF THE CURRENT ONE

A decentralized information space (for computers an and pe d people) A Web of A Web of data ta Consisting of information and non-information resources

 Uniquely identified  Connected to each other via hyperlinks  Accessed via the Internet  Created and used by different parties

slide-5
SLIDE 5

INFORMATION WITH WELL- DEFINED MEANING

Machines can process Web information ‘intelligently One can encode this additional information in a machine- processable way

Using formal knowledge representation languages and reasoning

This article is about a person This article is about a writer People and writers have charac acterist stic pr properties es e.g.,

  • they are born somewhere
  • they publish books
  • books have topics, chapters,

a price etc.

slide-6
SLIDE 6

COMPUTERS AND PEOPLE WORK IN COOPERATION

Artificial intelligence: “th the scienc ence a and eng engine neering ng of making ng i intel ellige gent machines”

[John Mc Carthy, http://www-formal.stanford.edu/jmc/whatisai/whatisai.html]

Areas of AI

 Knowledge representation  Inference  Logics  Search  Planning and scheduling  Pattern recognition  Learning  Natural language processing  Computer vision  Robotics  ....

slide-7
SLIDE 7

MEANING ON THE WEB

 Add metadat adata to Web resources  Dif ifferent t types o

  • f lin

links ks  Encode additional information about metadata entities and links in

  • nto

ntologies  No global bal information schemas  Incomple lete and inconsistent

[Examples from Wikipedia]

slide-8
SLIDE 8

YOU DON’T HAVE TO BUILD YOUR OWN ONTOLOGY

slide-9
SLIDE 9

LINKED DATA MAKES DATA INTEGRATION EASY

Concepts, entities, and properties are accessible on the Web just as traditional Web documents Linked Data: Linked Data: Set of technologies and principles to publish and access data on the Web http://lod-cloud.net/ http://www.ted.com/talks/tim_berners_lee _on_the_next_web.html

slide-10
SLIDE 10

SEMANTIC WEB STACK

Standardized family of languages

 Compatible with the Web architecture  With a formal semantics

Linked data is part of the stack Tools

 Editors  Data stores  Reasoners  Machine learning  NLP  Data interlinking  …

slide-11
SLIDE 11

FROM THE SEMANTIC WEB TO SEMANTIC TECHNOLOGIES

“The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries” [W3C]

slide-12
SLIDE 12

EXAMPLE: KNOWLEDGE GRAPH MAKING SEARCH MORE INTELLIGENT

http://googleblog.blogspot.gr/2012/05/introducing-knowledge-graph-things-not.html

slide-13
SLIDE 13

EXAMPLE: BBC & MEDIA CONTENT PUBLISHING AND INTEGRATION

slide-14
SLIDE 14

EXAMPLE: OPEN GRAPH INTEROPERABLE CONTENT REPRESENTATION

Represent Web content in a social graph in an interoperable way Used by Facebook (‘stories’), Google (snippets), IMDb etc. Facebook: actors, apps, objects with metadata to create stories

 Example: Elena has finished reading ‘The Economist’, an object of type Newspaper  Types with attributes, extensions allowed  Pre-defined and custom actions on objects

14

http://ogp.me/ Image from https://developers.facebook.com/docs/opengraph/cre ating-custom-stories

10/5/2015

slide-15
SLIDE 15

EXAMPLE: PROJECT HALO

05.10.2015

Ima mages f from http:/ ://w /www.projecthalo.com and d http:/ ://w /www.inquireproject.com/ m/

slide-16
SLIDE 16

EXAMPLE: DATA.GOV

USING LINKED DATA TO PUBLISH GOVERNMENT OPEN DATA

slide-17
SLIDE 17

SEMANTIC WEB TODAY

Semantic Web technologies, standardized by the W3C, are mature

 RDF recommendation in 1999, update in 2004  RDFa (RDF in HTML) note in 2008  RDFS recommendation in 2004  SPARQL recommendation in 2008  OWL recommendation in 2004, update in 2009

Schema.org markup (RDFa, microformats, microdata)

 http://www.webdatacommons.org/structureddata/

Linked Open Data

 http://linkeddatacatalog.dws.informatik.uni- mannheim.de/state/

Ontologies

 http://lov.okfn.org/dataset/lov/

slide-18
SLIDE 18

JOIN THE SEMANTIC WEB COMMUNITY

Mailing lists Mailing lists

 public_lod  semanticweb  Diverse others for special topics (Dbpedia, schema.org etc.)

Facebook, LinkedIn, Facebook, LinkedIn, Quora Conferences Conferences

 Academic: ESWC, ISWC, WWW, AAAI etc.  Applied/industry: Semantics, SmartData etc.

Workshops Workshops

 Knowledge Discovery and Data Mining Meets Linked Open Data @ESWC  Consuming Linked Data @ISWC  Detection, Representation, and Exploitation of Events in the Semantic Web @ESWC  Services and Applications over Linked APIs and Data @ESWC  Ontology Alignment Evaluation Initiative @ISWC  Semantic Statistics @ISWC  Ontology Design Patterns @ISWC  NLP & Dbpedia @ISWC  Linked Data for Information Extraction @ISWC  …