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Feb 19, 2014: NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations ...

Feb 19, 2014: NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations
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Ramanathan V. Guha, Google Fellow; Founder of Schema.org; Pierre-Paul Lemyre, Director of Business Development, Lexum; Bob Du Charme, Director of Digital Media Solutions, TopQuadrant

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NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations Presentation Transcript

  • 1. http://www.niso.org/news/events/2014/virtual/semantic/ NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations February 19, 2014 Speakers: Ramanathan V. Guha, Ralph Swick, Kevin Ford, Henry Story, Pierre-Paul Lemyre, Bob DuCharme
  • 2. NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations Agenda 11:00 a.m. – 11:10 a.m. – Introduction Todd Carpenter, Executive Director, NISO 11:10 a.m. - 12:00 p.m. Keynote Address
Ramanathan V. Guha, Google Fellow; Founder of Schema.org 12:00 p.m. - 12:45 p.m.: The W3C Semantic Web Initiative
Ralph Swick, Domain Lead of the Information and Knowledge Domain, W3C 12:45 p.m. - 1:30 p.m. Lunch Break 1:30 p.m. - 2:15 p.m. Semantic Web Applications in Libraries: The Road to BIBFRAME
Kevin Ford, Network Development & MARC Standards Office, Library of Congress 2:15 p.m. - 3:00 p.m.: The Social Data Graph: The Friend of a Friend (FOAF) Project
Henry Story, Chief Technical Officer & Co-founder at Stample 3:00 p.m. - 3:15 p.m. Afternoon Break 3:15 p.m. - 3:45 p.m. Sharing Information on the Semantic Web: The Need for a Global License Repository
Pierre-Paul Lemyre, Director of Business Development, Lexum 3:45 p.m. - 4:15 p.m. Semantic Web Applications in Publishing 
Bob Du Charme, Director of Digital Media Solutions, TopQuadrant 4:15 p.m. - 5:00 p.m. Conference Roundtable: Services that Build on Others Semantic Web Data: Semantic Search Beyond RDF
Moderated by: Todd Carpenter, Executive Director, NISO
  • 3. Towards a web of Data R.V.Guha Google
  • 4. Outline of talk • How did we end up here … a personal perspective – The tortuous path through standards and products • Schema.org – Why schema.org, principles, how does it work – Status : schemas, adoption, partners, applications • Reports from Google, Bing, Yahoo! & Yandex • Looking forward: Schemas in the pipeline, Research problems
  • 5. One day, 16 years ago, … • Ralph Swick, Ora Lasilla, Tim Bray, Eric Miller and myself • Trying to make sense of a flurry of activity – XML, MCF, CDF, Sitemaps, … • There were a number of problems – PICS, Meta data, sitemaps, … • But one unifying idea
  • 6. Context: The Web for humans Structured Data Web server HTML
  • 7. Goal: Web for Machines & Humans Structured Data Web server Apps
  • 8. What does that mean? Ryan, Oklahama Actor type birthplace Chuck Norris birthdate March 10th 1940
  • 9. How do we get there? • How does the author give us the graph – Data Model – Syntax – Vocabulary – Identifiers for objects • Why should the author give us the graph?
  • 10. Going depth first • Many heated battles – Lot of proposals, standards, companies, … • Data model – Trees vs DLGs vs Vertical specific vs who needs one? • Syntax – XML vs RDF vs json vs … • Model theory anyone – We need one vs who cares vs what’s that?
  • 11. Timeline of ‘standards’ • • • • • • ‘96: Meta Content Framework (MCF) (Apple) ’97: MCF using XML (Netscape)  RDF, CDF ’99 -- : RDF, RDFS ’01 -- : DAML, OWL, OWL EL, OWL QL, OWL RL ’03: Microformats And many many many more … SPARQL, Turtle, N3, GRDDL, R2RML, FOAF, SIOC, SKOS, … • Lots of bells & whistles: model theory, inferencing, type systems, …
  • 12. But something was missing … • Fewer than 10k sites were using these standards • Something was clearly missing and it wasn’t more language features • We had forgotten the ‘Why’ part of the problem • The RSS story
  • 13. ’07 - :Rise of the consumers • Yahoo! Search Monkey, Google Rich Snippets, Facebook Open Graph • Offer webmasters a simple value proposition • Search engines to webmasters: – You give us data … we make your results nicer • Usage begins to take off – 1000x increase in markup’ed up pages in 3 years
  • 14. Yahoo Search Monkey • Give websites control over snippet presentation • Moderate adoption – Targeted at high end developers – Too many choices
  • 15. Yandex Enhanced Snippets & Services • Snippets for Web Search • Data for Vertical Search Services • Various syntax and vocabularies • Yandex specific vocabs for unexamined use cases
  • 16. Google Rich Snippets: Reviews
  • 17. Google Rich Snippets: People
  • 18. Google Rich Snippets: Events
  • 19. Google Rich Snippets: Recipes
  • 20. Google Rich Snippets: Recipe View
  • 21. Google Rich Snippets • • • • • • Multi-syntax Adhoc vocabulary for each vertical Very clear carrot Lots of experimentation on UI Moderately successful Scaling issues with vocabulary
  • 22. Situation in 2010 • Too many choices/decisions for webmasters – Divergence in vocabularies • Too much fragmentation • N versions of person, address, … • A lot of bad/wrong markup – ~25% for microformats, ~40% with RDFA – Some spam, mostly unintended mistakes • Absolute adoption numbers still rather low – Less than 100k sites
  • 23. In the meantime … • The Web has grown – >25 million independent active sites – Trillions of web pages – ~5 billion pages change every day – Spam, malware, … • In other words: scaling problems along every dimension
  • 24. Schema.org • Work started in August 2010 – Google, Yahoo!, Microsoft & then Yandex • Goals: – One vocabulary understood by all the search engines – Make it very easy for the webmaster • It is A vocabulary. Not The vocabulary. – Webmasters can use it together other vocabs – We might not understand the other vocabs. Others might
  • 25. Schema.org: report card • Over 15% of all sites/pages now have schema.org • Over 5 million sites, over 25 billion entity references • In other words – Same order of magnitude as the web – Not just ‘promising new stuff’ anymore % urls % urls
  • 26. Schema.org: Major sites • • • • • • • • • • News: Nytimes, guardian.com, bbc.co.uk, Movies: imdb, rottentomatoes, movies.com Jobs / careers: careerjet.com, monster.com, indeed.com People: linkedin.com, Products: ebay.com, alibaba.com, sears.com, cafepress.com, sulit.com, f otolia.com Videos: youtube, dailymotion, frequency.com, vinebox.com Medical: cvs.com, drugs.com Local: yelp.com, allmenus.com, urbanspoon.com Events: wherevent.com, meetup.com, zillow.com, eventful Music: last.fm, myspace.com, soundcloud.com
  • 27. Schema.org: categories • Most used categories by occurrence – Person, Offer, Product, PostalAddress, VideoObject, Image Object, BlogPosting, WebPage, Article, AggregateRating, Lo calBusiness, Place, Organization, MusicRecording, JobPosti ng, Recipe, Book, Movie, Blog, Photograph, ImageGallery • Most used categories by domains – ImageObject, WebPage, PostalAddress, BlogPosting, Produ ct, Person, Offer, Article, LocalBusiness, Organization, Blog, AggregateRating, Review, VideoObject, Place, Event, Rating , AudioObject, MusicRecording, Store
  • 28. Schema.org: properties • Top properties by occurrence – name, url, image, description, offers, author, price, thumb nailUrl, datePublished, addressLocality, address, itemOffer ed, duration, streetAddress, isFamilyFriendly, priceCurrenc y, playerType, paid, regionsAllowed, postalCode, hiringOrg anization, jobLocation, • Top properties by domain – Name, description, url, image, contentURL, address, author , telephone, price, postalCode, offers, ratingValue, priceCur rency, datePublished, addressRegion, availability, email, be stRating, creator, review, location, startDate
  • 29. Schema.org principles: Simplicity • Simple things should be simple – For webmasters, not necessarily for consumers of markup – Webmasters shouldn’t have to deal with N namespaces • Complex things should be possible – Advanced webmasters should be able to mix and match vocabularies • Syntax – Microdata, usability studies – RDFa, json-ld, …
  • 30. Schema.org principles: Simplicity • Can’t expect webmasters to understand Knowledge Representation, Semantic Web Query Languages, etc. • It has to fit in with existing workflows • Avoid KR system driven artifacts – domainIncludes/rangeIncludes – No classes like ‘Agent’ – Categories and attributes should be concrete
  • 31. Schema.org principles: Simplicity • Copy and edit as the default mode for authors – It is not a linear spec, but a tree of examples • Vocabularies – Authors only need to have local view – But schema.org tries to have a single global coherent vocabulary
  • 32. Schema.org principles: Incremental • Started simple – ~ 100 categories at launch • Applies to every area – Add complexity after adoption – now ~1200 vocab items – Go back and fill in the blanks • Move fast, accept mistakes, iterate fast
  • 33. Schema.org Principles: URIs • ~1000s of terms like Actor, birthdate – ~10s for most sites – Common across sites • ~10ks of terms like USA – External enumerations Ryan, Oklahama Actor type birthplace Chuck Norris • ~10m-100m terms like Chuck Norris and Ryan, Oklahama – Cannot expect agreement on these USA – Consumers can reconcile entity references citizenOf birthdate March 10th 1940
  • 34. Schema.org Principles: Collaborations • Most discussions on public W3C lists • Work closely with interest communities • Work with others to incorporate their vocabularies – We give them attribution on schema.org – Webmasters should not have to worry about where each piece of the vocabulary came from – Webmasters can mix and match vocabs
  • 35. Schema.org Principles: Collaborations • • • • • • • • • • IPTC /NYTimes / Getty with rNews Martin Hepp with Good Relations US Veterans, Whitehouse, Indeed.com with Job Posting Creative Commons with LRMI NIH National Library of Medicine for Medical vocab. Bibextend, Highwire Press for Bibliographic vocabulary Benetech for Accessibility BBC, European Broadcasting Union for TV & Radio schema Stackexchange, SKOS group for message board Lots and lots and lots of individuals
  • 36. Schema.org Principles: Partners • Partner with Authoring platforms – Drupal, Wordpress, Blogger, YouTube • Drupal 8 – Schema.org markup for many types • News articles, comments, users, events, … – More schema.org types can be created by site author – Markup in HTML5 & RDFa Lite – Coming out early 2014
  • 37. Schema.org & W3C • Web Schemas – vocabulary discussion Part of W3C Semantic Web Interest Group  • W3C Community Group(s) e.g. http://w3.org/community/schemabibex/  • Wiki, Mercurial, email lists, … Informal collaboration rather than classic standardization Ideas for extending schema.org welcomed Also room this week Thu/Fri, see blog.schema.org   
  • 38. W3C Data Activity • Subsumes & expands on Sem Web & eGov • Success for Sem Web in several communities who publish self-describing, re-usable structured data • schema.org makes it easier for developers to use common vocabulary • W3C pleased with the success of schema.org and continues to encourage data formats that support mixing of vocabs
  • 39. Schema.Org Usage @ MS Today • Used primarily to support Bing’s Rich Captions • Also recommended for Windows 8 App Contracts Tomorrow • Extended usage in Rich Captions and Bing Search • Development support via new Bing Platform Dev Center • Innovative experiences in other Microsoft products
  • 40. Bing Rich Captions • Ensure your Schema.Org annotations represent the primary content on the page -> ensures correct caption data is shown • Ensure your Schema.Org annotations are appropriate and relevant to the content of the page -> improves your chances of being shown • Rich Captions do not support RDFa 1.1 or JSONLD -> stick with RDFa 1.0 or Microdata for now
  • 41. Yahoo! Search • Rich results – Blanco et al. Enhanced Results for Web Search, SIGIR 2011 – Mika & Potter. Metadata Statistics for a Large Web Corpus, LDOW 2012 • Related entities – Blanco et al. Entity recommendations in Web Search – Wed 15:15 Search track
  • 42. Yahoo! Media • Schema.org across the Yahoo! Network – Article, Photo and Video markup – Q&A and Sports under development • Personalization – Based on past reading behavior, Facebook profile, implicit feedback – Concepts from a news taxonomy and entity-graph – Nicolas Torzec. The Y! Knowledge Base: Making Knowledge Reusable at Yahoo! SemTech 2013
  • 43. Applications • Applications drive adoption • First generation of applications – Rich presentation of search results • Many new applications are coming up – On search page and beyond
  • 44. Newer Applications: Knowledge Graph
  • 45. Newer Applications: Knowledge Graph
  • 46. Non web search Applications • Searching for Veteran friendly jobs
  • 47. Non search applications: Google Now
  • 48. Pinterest: Schema.org for Rich Pins
  • 49. Non search Applications • Open Table website  confirmation email  Android Reminder
  • 50. Future application • Clinical trials • 4000+ clinical trials at any time in the US alone • Almost all the data ‘thrown away’ • All that gets published is a textual ‘abstract’ • Over half the trials are redundant • Earlier trials have the data • Assumptions, etc. cannot be re-examined • Longitudinal studies extremely hard, but super
  • 51. Vocabularies in the pipeline • • • • Actions (Potential Actions), Events Accessibility Commerce: Orders, Reservations, … Communication: Fleshing out TV, Radio, Email, Q&A, … • Media: Scholarly works, Comics, Serials • Sports • and many many more …
  • 52. Big initiatives underway • Representing time – Lot of triples with associated time interval – Hard to force into the triple model – Looking at named graphs • Tabular / CSV data – Census data, Scientific data, etc. – Need mechanisms for external specification of the meaning of these tables
  • 53. Research Problems • Propositional Attitudes – Fictional characters – Possible actions • Mapping Entities across sites – Billions of entities – But hundreds of billions of entity references – Very large scale cross site entity mapping
  • 54. Schema.org: concluding • Provide webmasters – A good reason for adding structured data markup – Allow more advanced authors to add much richer markup from many different vocabularies – An easy way to do it • Many different applications emerging • Many interesting research problems left • Light at the end of the tunnel
  • 55. Questions?
  • 56. MCF • Introduced the Directed Labeled Graph (DLG) model • Used to describe site structure • Provided simple visualization plugin • ~3m downloads, few thousand sites (not too bad for ‘96) • Documentation was about a page • Lead to MCF using XML (4 pages including diagrams)
  • 57. 97-99: RDF / RDFS • MCF (4 pages) got renamed to RDF • Data model becomes a religion • Lot of new things … reification, sequences, chains, model theory, … • Final version done only by 2002 • RDF Primer is 100 pages long! • But … used by very few websites
  • 58. ‘99: RSS 0.91 • Netscape needs customizable home page ala my.yahoo • No $s to do deals • Create format and open it up to everyone – 2 year target was 5k feeds – 14 years later  100m+ feeds
  • 59. 2002: OWL • Tim BL, et. al. ‘Semantic Web’ paper • Answer to lack of adoption: we need more features • Lots of Darpa and EU funding • Very powerful solutions … looking for problems • ~10 years later … < 100 sites use it
  • 60. ‘04-07: Microformats • Reaction to the complexity of RDF & OWL • Retrofitted vCard, etc. into HTML  hCard • Adoption by Google & Yahoo! made it wildly successful • Evolutionary dead-end: no new vocabulary in over 5 yrs.
  • 61. Lessons • The RSS story • Make sure you have your carrot! – Carrots work much better than sticks! • Find the right initial level of generality • Start simple and iterate fast • Optimize for flexibility
  • 62. Information on the Semantic Web: The Need for a Global License Repository
  • 63. Distributed Production of Information • Originates in research (not commercial venture) • Access is its driving force (not property) • Has defined the Internet as we know it – Open standards & Open Source Software (OSS) – Web 2.0 – And now the semantic web
  • 64. Distributed Production of Information Why does it work?
  • 65. OSS • Motives to produce OSS – Ethical / policy reasons – Non-monetary incentives – Increase the speed of market adoption – Co-create and appropriate value • By building on previous works • By integrating external contributions
  • 66. OSS • Each contributor is adding to the pool of knowledge available to all • This knowledge is more valuable than what any contributor can achieve individually • Not new phenomenon (science, music, education, ...)
  • 67. OSS - Legal Framework • Collaborative software development existed before – Under informal agreements – Under bilateral contractual schemes • OSS licenses created a favourable legal environment – By favouring reciprocity (BSD) – By securing openness (GPL)
  • 68. OSS • What the success of OSS makes us see clearly – In a networked world, centralized corporate production can sometime be surpassed by distributed production – Mass adoption requires a proper legal framework
  • 69. OSS - Legal Framework Is this conclusion applicable only to software?
  • 70. Web 2.0 • Extensive use of Web services facilitating mass collaboration – Rich Internet applications – Web forums – Blogs – Wikis – Folksonomies (Social tagging)
  • 71. Web 2.0 • Web-as-participation-platform – Architecture of participation – Users become producers – Collective intelligence
  • 72. Web 2.0 Legal Framework • Adaptation of OSS licenses – GNU Free Documentation License – OpenContent License – Creative Commons (CC)
  • 73. Web 2.0 Legal Framework • Extended the favourable environment to all types of freely accessible information – By specifying the applicable reuse conditions (no permission required) – By clarifying the spectrum of potential rights – By standardizing the licensing process and making automated retrieval possible (CC)
  • 74. Web 2.0 Legal Framework The question of online right management should be solved by now?
  • 75. Semantic web • Collaborative initiatives developed independently from each other – Vertical information flow (Information silos) • Accessibility & reusability of information call for reciprocity between them and other sources of information – Horizontal information flow (seamless interoperability)
  • 76. Semantic Web • Using technology for data sharing and reuse – Data modeling (XML, RDF) – Syndication technologies (RSS) – Ontologies (OWL) – Heuristic (text-recognition)
  • 77. Semantic Web • Web understandable by computers – Data get a meaning – Dynamic discovery, composition and execution – Layers of services
  • 78. Semantic Web • Current state of implementation – Information sources are pre-qualified (limited dynamic discovery) • Public domain sources • Use of APIs under bilateral agreements – Reproduction of information is often limited (links are the norm, mashup still the exception) – Management of personal information is a nightmare
  • 79. Semantic web Legal Framework Here again successful mass adoption is not just about technology
  • 80. The Legal Issue: Fragmentation of Rights • Rights are fragmented – By reuse conditions – By jurisdictions – By formats / domains • Scalable to the smallest element (website > webpage > data)
  • 81. The Legal Issue: Fragmentation of Rights • Not a new problem – Requests to limit the proliferation of OSS licenses (FSF) – Initiatives to standardize licensing (CC) • Not fundamental as long as humans are in charge of the reuse of information
  • 82. The Legal Issue: Fragmentation of Rights • Seamless interoperability through semantic technologies require computers to automatically – Retrieve applicable licenses – Resolve their respective terms – Select information with adequate conditions for the anticipated reuse
  • 83. The Legal Issue: Fragmentation of Rights • Standardization under CC is helping – Embedded license information ease their retrieval – Computer readable version ease their resolution – Standardized terminology and low number ease the selection
  • 84. The Legal Issue: Fragmentation of Rights • But it is a partial solution – Most content is not licensed under CC (and often cannot) – Copyright holders have the right to attach alternative conditions to their content – CC is not generally accepting alternative licenses • Example of difficulties – Website Terms of Use vs Google “Usage rights” feature
  • 85. The Legal Issue: Fragmentation of Rights A higher level resolution mechanism is required
  • 86. The Solution: A Global License Repository? • A database of varying copyright licenses and their conditions • A standardized approach to licenses resolution and selection (expand the CC model?) • A Web service that can be queried by users and computers
  • 87. The Solution: A Global License Repository? • Issues that need to be addressed – Unlimited number of reuse conditions – Format and domain specific restrictions – Internationalization – Versioning of licenses – Compatibility between licenses (relicensing)
  • 88. The Solution: A Global License Repository? • Possible solutions – Organizing conditions and restrictions into groups or categories? – Managing licenses at the lowest possible level and associating related ones? – Limiting the designation of compatibility to the most common licenses?
  • 89. The Solution: A Global License Repository? • A successful implementation requires – Promotion and large-scale adoption of a standard tagging model for information – Involvement of copyright holders in feeding and updating the database – Transparency and quality control procedures generating trust in the system
  • 90. The Solution: A Global License Repository? • A successful implementation requires – Scalability insuring efficient interactions at every level of development – Provision of outputs under standardized formats – Provision of simple APIs facilitating interactions with the repository
  • 91. The Solution: A Global License Repository? • Architecture – Use of open standards and OSS to display transparency – Use of collaborative technologies to distribute updates – Use of aggregative technologies to promote exploitation and reuse
  • 92. Conclusion • Facilitating the sharing of information is the core function of the Internet – Semantic web is a no-brainer in principle • But mass adoption requires a legal framework – Providing a lawful mean to co-create and appropriate value – Securing the existing rights of copyright holders
  • 93. ‹#›
  • 94. Contact Let me know what you think! Pierre-Paul Lemyre <lemyrep@lexum.com>
  • 95. Semantic Web Applications in Publishing Bob DuCharme February 19, 2014 © Copyright 2014 TopQuadrant Inc. 100
  • 96. Outline  http://snee.com/rdf/niso2014/  RDF  Taxonomies, semantics, and content  Metadata management  Content creation © Copyright 2014 TopQuadrant Inc. 101
  • 97. RDF ● ● ● ● ● Resource Description Format W3C Standard Syntaxes exist, but ultimately a data model Very easy to aggregate Tools exist to treat relational data and spreadsheets as RDF © Copyright 2014 TopQuadrant Inc. Slide 102
  • 98. An RDF “statement”: the triple ● (Subject, predicate, object) ● “This resource, for this property, has this value.” ● “John Smith has a hire date of 2012-10-11.” ● “Chair 523 located in in room 47.” ● “index.html has the title ‘My Home Page'.” © Copyright 2014 TopQuadrant Inc. Slide 103
  • 99. Using URIs A triple? Subject: index.html Predicate: title Object: “My Home Page” © Copyright 2014 TopQuadrant Inc. Slide 104
  • 100. Using URIs* A triple? Subject: index.html Predicate: title Object: “My Home Page” A triple! Subject: <http://www.myco.com/members/index.html> Predicate: <http://purl.org/dc/elements/1.1/title> Object: “My Home Page” *Uniform Resource Identifier, not Uniform Resource Locator—an identifier, not an address. © Copyright 2014 TopQuadrant Inc. Slide 105
  • 101. URIs as triple objects <urn:isbn:9780062515872> <http://purl.org/dc/elements/1.1/creator> “Tim Berners-Lee” . or… <urn:isbn:9780062515872> <http://purl.org/dc/elements/1.1/creator> <http://topbraid.org/ids/TimBernersLee> . or… <urn:isbn:9780062515872> <http://purl.org/dc/elements/1.1/creator> <http://www.w3.org/People/Berners-Lee/card#i> . © Copyright 2014 TopQuadrant Inc. Slide 106
  • 102. URIs as triple objects <urn:isbn:9780062515872> <http://purl.org/dc/elements/1.1/creator> <http://www.w3.org/People/Berners-Lee/card#i> . <http://www.w3.org/People/Berners-Lee/card#i> <http://www.w3.org/2000/01/rdf-schema#label> “Tim Berners-Lee” . 107 © Copyright 2014 TopQuadrant Inc. Slide 107
  • 103. URIs as triple objects <urn:isbn:9780062515872> <http://purl.org/dc/elements/1.1/creator> <http://www.w3.org/People/Berners-Lee/card#i> . <http://www.w3.org/People/Berners-Lee/card#i> <http://www.w3.org/2000/01/rdf-schema#label> “Tim Berners-Lee” . < http://www.w3.org/People/Berners-Lee/card#i> <http://xmlns.com/foaf/0.1/mbox> “timbl@w3.org” . < http://www.w3.org/People/Berners-Lee/card#i> <http://dbpedia.org/property/almaMater> <http://dbpedia.org/resource/The_Queen's_College,_Oxford> . <http://dbpedia.org/resource/The_Queen's_College,_Oxford> <http://dbpedia.org/property/established> 1341 . 108 © Copyright 2014 TopQuadrant Inc. Slide 108
  • 104. A “graph” “Tim Berners-Lee” http://www.w3.org/2000/01/rdf-schema#label urn:isbn:9780062515872 http://purl.org/dc/elements/1.1/creator http://www.w3.org/People/BernersLee/card#i http://dbpedia.org/property/almaMater http://dbpedia.org/resource/The_Queen's_College,_Oxford http://dbpedia.org/property/established 1341 © Copyright 2014 TopQuadrant Inc. http://xmlns.com/foaf/0.1/m box “timbl@w3.org” Slide 109
  • 105. SPARQL: look for patterns within the graph “Tim Berners-Lee” http://www.w3.org/2000/01/rdf-schema#label urn:isbn:9780062515872 http://purl.org/dc/elements/1.1/creator http://www.w3.org/People/BernersLee/card#i http://dbpedia.org/property/almaMater http://dbpedia.org/resource/The_Queen's_College,_Oxford http://dbpedia.org/property/established 1341 © Copyright 2014 TopQuadrant Inc. http://xmlns.com/foaf/0.1/m box “timbl@w3.org” Slide 110
  • 106. Defining structure ● Optional ● Nice option in “schema vs. schemaless” debate ● RDFS (RDF Schema Language) ● Enumerate classes, properties, and relationships between them # Prefixes stand in for base URIs, like with XML foaf:Person rdf:type owl:Class . viaf:19831453 rdf:type foaf:Person . ● OWL (Web Ontology Language) ● ● – Further describe classes and properties, enable fancier inferencing For example: Jack has a spouse value of Jill; "spouse" is an owl:SymmetricProperty; software can then infer that Jill has a spouse value of Jack. (Semantics!) Both are W3C standards, both expressed with triples (and therefore easy to aggregate) © Copyright 2014 TopQuadrant Inc. Slide 111
  • 107. Simple Knowledge Organization System  Controlled vocabulary  Taxonomy  Thesaurus  Ontology © Copyright 2014 TopQuadrant Inc. 112
  • 108. Taxonomies Mammal Horse Cat Dog metadata! Bulldog Collie Above: subset-of relationship. Alternatives: part-of, instance-of. © Copyright 2014 TopQuadrant Inc. 113
  • 109. Thesaurus Mammal Horse Cat Dog (use for: mutt, cur) Related term Bulldog Collie Building Residential Doghouse © Copyright 2014 TopQuadrant Inc. Commercial House 114
  • 110. Simple Knowledge Organization System  Controlled vocabulary  Taxonomy  Thesaurus  Ontology © Copyright 2014 TopQuadrant Inc. 115
  • 111. Simple Knowledge Organization System  Controlled vocabulary  Taxonomy  Thesaurus  Ontology © Copyright 2014 TopQuadrant Inc. SKOS: the W3C’s OWL ontology for creating thesauri, taxonomies, and controlled vocabularies. 116
  • 112. SKOS: standardized properties http://myCompany.com/animals/c43209101 preferred label (English): "dog" preferred label (Spanish): "perro" preferred label (French): "chien" alternative label (English): "mutt" alternative label (Spanish): "chucho" history note: "Edited by Jack on 5/4/11" related term: http://myCompany.com/shelters/c3048293 © Copyright 2014 TopQuadrant Inc. 117
  • 113. SKOS: custom properties http://myCompany.com/animals/c43209101 preferred label (English): "dog" preferred label (Spanish): "perro" preferred label (French): "chien" alternative label (English): "mutt" alternative label (Spanish): "chucho" history note: "Edited by Jack on 5/4/11" related term: http://myCompany.com/shelters/c3048293 product: http://myCompany.com/vaccinations/c2197503 foo code: “5L-MN1-003” © Copyright 2014 TopQuadrant Inc. 118
  • 114. Who is using SKOS?  AGROVOC  New York Times: People, Organizations, Locations, Subject Descriptors  Library of Congress subject headers  AGFA drug admin. forms  NASA: many categories © Copyright 2014 TopQuadrant Inc. 119
  • 115. TopQuadrant’s TopBraid EVN © Copyright 2014 TopQuadrant Inc. 120
  • 116. Metadata management  Content metadata is more than just keywords  3 Vs of Big Data: Volume, Velocity and Variety  Gartner October 2013 poll on which is most difficult:  Variety: 16%  Volume: 35%  Variety: 49% © Copyright 2014 TopQuadrant Inc. 121
  • 117. Metadata in images from 3 suppliers Supplier 1 Supplier 2 Supplier 3 file name file name file name file size file size file size last modified last modified last modified shutter speed shutter speed GPS latitude GPS latitude GPS longitude GPS longitude creator name X resolution X resolution Horz. resolution Y resolution Y resolution Vertical resolution copyright notice keywords aperture setting re-use rights subject F stop (etc.) © Copyright 2014 TopQuadrant Inc. 122
  • 118. Accommodating overlapping metadata  Store it all; each is just a triple  Identify relationships for easier searching  “aperture setting” = “F stop”  “copyright notice” a subproperty of “re-use rights”  “Y resolution” a subproperty of “vertical resolution”  Image data a simple case. Consider audio, video, management of digital rights… © Copyright 2014 TopQuadrant Inc. 123
  • 119. Metadata and… Prosopography: the study of careers, especially of individuals linked by family, economic, social, or political relationships. © Copyright 2011 TopQuadrant Inc. Slide 124
  • 120. Content creation From an article on Massive Open Online Courses in the February 8, 2014 Economist: "...says Tylor Cowen, a cofounder of Marginal Revolution University, it is possible that textbook publishers are better equipped than universities to develop MOOCs profitably." © Copyright 2014 TopQuadrant Inc. 125
  • 121. Available Data: The Linked Open Data Cloud © Copyright 2014 TopQuadrant Inc. Slide 126
  • 122. Wikipedia page on Andrew Johnson © Copyright 2014 TopQuadrant Inc. Slide 127
  • 123. DBpedia page for Andrew Johnson (further down the page…) © Copyright 2014 TopQuadrant Inc. Slide 128
  • 124. Thank you! bducharme@topquadrant.com http://snee.com/rdf/niso2014/ © Copyright 2014 TopQuadrant Inc. 129
  • 125. NISO Virtual Conference The Semantic Web Coming of Age: Technologies and Implementations Questions? All questions will be posted with presenter answers on the NISO website following the webinar: http://www.niso.org/news/events/2014/virtual/semantic/ NISO Virtual Conference • February 19, 2014
  • 126. THANK YOU Thank you for joining us today. Please take a moment to fill out the brief online survey. We look forward to hearing from you!