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The Social Semantic Web


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ICWSM Tutorial / Washington, DC, USA / 23rd May 2010

ICWSM Tutorial / Washington, DC, USA / 23rd May 2010

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    • 1. The Social Semantic Web:ICWSM Tutorial
      Alexandre Passant
      John Breslin
    • 2. Introduction
      Why is this important?
    • 3. The Social Web
      is exploding!
      image from
    • 4. 61% = social networks
      11% = forums
      11% = UG content sites, e.g.
      10% = UG marketplaces, e.g.
      03% = blogs
      01% = UG reviews, e.g.
      01% = wikis
      02% = other
      text from
    • 5. Sites go up...
      image from
    • 6. Facebook and Twitter
    • 7. ...and sites come down
      image from
    • 8. Bebo
    • 9. Object-centred sociality (AKA social objects) gives some explanations
      Users are connected via a common object:
      Their job, university, hobbies, interests, a date…
      “According to this theory, people don’t just connect to each other. They connect through a shared object. […]Good services allow people to create social objects that add value.” – JyriEngestrom
      Flickr = photos = bookmarks
      Blogs = discussion posts
    • 10. It’s the social objects we create…
    • 11. …that connect usto other people
    • 12. Boom!
    • 13. image from
      The amount of stuff out there is vast
    • 14. Social websites are like data silos
      image from
    • 15. Many isolated communities of users and their data
      image from
    • 16. Need ways to connect these islands
      image from
    • 17. Allowing users to easily move from one to another
      image from
    • 18. Enabling users to easily bring their data with them
      image from
    • 19. Semantics
    • 20.
    • 21.
    • 22. The Semantic Web
      A brief overview
    • 23. What’s in a page ? And in a link ?
    • 24. Tim Berners-Lee, The 1st World Wide Web Conference, Geneva, May 1994
      To a computer, the Web is a flat, boring world, devoid of meaning. This is a pity, as in fact documents on the Web describe real objects and imaginary concepts, and give particular relationships between them. […] Adding semantics to the Web involves two things: allowing documents which have information in machine-readable forms, and allowing links to be created with relationship values. Only when we have this extra level of semantics will we be able to use computer power to help us exploit the information to a greater extent than our own reading.
    • 25. Aims of the Semantic Web
      Bridging the gap between a Web of Documents to a Web of Data, with typed objects and typed relationships
      Adding machine-readable metadata to existing content, so that information can be parsed, queried, reused
      Defining shared semantics for this metadata to allow interoperability between applications and for advanced purposes, such as reasoning
      Enabling machine-readable knowledge at Web scale, making information more easy to find and process
    • 26. A bit of history
      Memex, Vannevar Bush, 1945:
      “A device in which an individual stores all his books, records, and communications.”
      Augmenting Human Intellect, Douglas Engelbart, 1960:
      “By ‘augmenting human intellect’ we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems.”
    • 27. The Semantic Web, circa 2010
      Most standardisation work is done in the W3C:
      The Semantic Web activity:
      Incubator Groups, Working Group, Interest Groups:
      WGs for SPARQL, RDB2RDF, RIF, etc.
      HCLS IG, Social Web XG, etc.
    • 28. image from
      The Semantic Web stack
    • 29. Identifying resources with URIs
      URIs are used to identify everything in a unique and non-ambiguous way:
      Not only pages (as on the current Web), but any resource (people, documents, books, interests, etc.)
      A URI for a person is different from a URI for a document about the person, because a person is not a document!
    • 30. Defining assertions with RDF
      URIs identify resources:
      How do we define assertions about these resources?
      We use RDF (Resource Description Framework):
      A data model; a directed, labeled graph using URIs
      Various serialisations (RDF/XML, N3, RDFa, etc.)
      RDF is based on triples:
      <subject> <predicate> <object> .
    • 31. RDF by example
      @prefix dct: <> .
      dct:title“Introduction to the Semantic Web” ;
      dct:author <> ;
      dct:subject <> .
    • 32. RDFa
      A way of embedding RDF in (X)HTML documents:
      One page for both humans and machines
      Don’t need to repeat yourself
      Introducing new XHTML attributes
      Current work is ongoing on RDFa 1.1:
      For profiles, etc.
    • 33. RDFa example
    • 34. Defining semantics with ontologies
      RDF provides a way to write assertions about URIs:
      But what about the semantics of these assertions, e.g. to state that identifies an acquaintance relationship?
      Ontologies provide common semantics for resources on the Semantic Web:
      “An ontology is a specification of a conceptualization”
      RDFS and OWL have different expressiveness levels
    • 35. Ontologies consist mainly of classes and properties
      :Person a rdfs:Class .
      :father a rdfs:Property .
      :father rdfs:domain :Person .
      :father rdfs:range :Person .
    • 36. Metadata and ontologies
    • 37. Notable ontologies
      Social networks and social data:
      FOAF, SIOC
      Software development:
      Comprehensive / top-level:
      Yago, OpenCYC
      Taxonomies and controlled vocabularies:
    • 38. Linked Data
      Building a “Web of Data” to enhance the current Web
      Exposing, sharing and connecting data about things via dereferenceable URIs
      The Linking Open Data (LOD) project:
      Translating existing datasets into RDF and linking them together, for example DBpedia (Wikipedia) and GeoNames, Freebase, BBC programmes, etc.
      Governement data also available as Linked Data
    • 39. The LOD cloud
    • 40. The LOD cloud
    • 41. image from
    • 42. Representation models for the Social Semantic Web
      Using ontologies to model social data
    • 43. Semantics can help social websites, and vice versa
      By using agreed-upon semantic formats to describe people, content objects and the connections that bind them all together, social media sites can interoperate by appealing to common semantics
      Developers are already using semantic technologies to augment the ways in which they create, reuse, and link profiles and content on social media sites (using FOAF, XFN / hCard, SIOC, etc.)
      In the other direction, object-centered social networks can serve as rich data sources for semantic applications
    • 44. The Social Semantic Web
    • 45. FOAF
      Friend Of A Friend
    • 46.
    • 47. FOAF (Friend-of-a-Friend)
      An ontology for describing people and the relationships that exist between them:
      Identity, personal profiles and social networks
      Can be integrated with other SW vocabularies
      FOAF on the Web:
      LiveJournal, MyOpera,, MyBlogLog, hi5, Fotothing, Videntity, FriendFeed, Ecademy, Typepad
    • 48. FOAF (Friend-of-a-Friend)
    • 49. FOAF (Friend-of-a-Friend)
    • 50. FOAF at a glance
    • 51. FOAF from Flickr
    • 52. FOAF from Twitter
    • 53. Exporting FOAF data
      And many more (Drupal 7, WordPress plug-ins, etc.)
    • 54. Distributed identity with FOAF
    • 55. Interlinking identities and networks
    • 56. Cross-site social recommendations with FOAF
    • 57. Distributed authentication with FOAF+SSL
    • 58. SIOC
      Semantically-Interlinked Online Communities
    • 59. SIOC, pronounced shock
      image from
    • 60. Semantically-Interlinked Online Communities (SIOC)
      An effort from DERI, NUI Galway to discover how we can create / establish ontologies on the Semantic Web
      Goal of the SIOC ontology is to address interoperability issues on the (Social) Web
      SIOC has been adopted in a framework of 50 applications or modules deployed on over 400 sites
      Various domains: Web 2.0, enterprise information integration, HCLS, e-government
    • 61. 61
      The aims of SIOC
      To “semantically-interlink online communities”
      To fully describe content / structure of social websites
      To create new connections between online discussion posts and items, forums and containers
      To enable the integration of online community info
      To browse connected Social Web items in interesting and innovative ways
      To overcome the chicken-and-egg problem with the Semantic Web
    • 62.
    • 63. Some of the SIOC core ontology classes and properties
    • 64. 64
    • 65. Designed to fit with other ontologies
    • 66. Combining SIOC and FOAF
    • 67.
    • 68. 68
      From last October
    • 69. SIOC and other RDFa in Drupal 7
      Drupal is a CMS used by,,,
      Two alpha versions of Drupal 7 released already, Semantic Web support built-in (RDFa)
      Full version expected soon
    • 70. Semantic search
    • 71. 71
    • 72. 72
      Find out more about the SIOC project
    • 73. Semantic presence
      Modeling presence and status updates using semantics
    • 74. Motivations
      There is a need to unify presence information and status notification processes across different services:
      Twitter, Facebook, Foursquare, etc.
      We can solve the information overload issue at the same time, by providing a means to identify who / which community the information should reach
    • 75. Online PresenceOntology
      @@ TODO
    • 76. The OPO model
    • 77. Sharing spaces allow us to…
      Solve the identity fragmentation problem related to status messages sharing:
      We may not want to share the same information to different people
      Model whom information is directed to:
      e.g. “Social media-aware people”, “Family contacts”, “Good friends”, “Work colleagues”, etc.
      Build with OPO, using rules defined in SPARQL, the query language for RDF
    • 78. @prefix rdf: <>.@prefix opo: <>.@prefix foaf: <>.@prefix sioc: <>.
      :Fred rdf:typefoaf:Agent;foaf:mbox <>.
      :myCustomMessagerdf:typesioc:Post;sioc:content "anybody in for a drink tonight?".
      :MyCurrentPresencerdf:typeopo:OnlinePresence;opo:customMessage :myCustomMessage;opo:startTime "2008-03-01T18:51:19";opo:intendedFor<>
      :Betty opo:declaresOnlinePresence :MyCurrentPresence.
    • 79. @prefix rdf: <>.@prefix opo: <>.@prefix foaf: <>.@prefix sioc: <>.
      :Fred rdf:typefoaf:Agent;foaf:mbox <>.
      :myCustomMessagerdf:typesioc:Post;sioc:content "anybody in for a drink tonight?".
      :MyCurrentPresencerdf:typeopo:OnlinePresence;opo:customMessage :myCustomMessage;opo:startTime "2008-03-01T18:51:19";opo:intendedFor <>
      :Betty opo:declaresOnlinePresence :MyCurrentPresence.
      PREFIX foaf: <>
      PREFIX rdf: <>
      PREFIX rel: <>
      <> rdf:typeopo:SharingSpace;
      { ?person rel:friendOf_ <> } UNION
      { ?person rel:spouseOf_ <> } UNION
      { ?person rel:childOf_ <> } .
      ?person foaf:basedNear <> .
    • 80. Semantic tagging
      Bridging the gap between folksonomies and ontologies
    • 81. Tagging issues
      Tagging enables user-generated classification of content with evolving and user-driven vocabularies
      But it also raises various issues:
      Tag ambiguity:
      “apple” = fruit or computer brand?
      Tag heterogeneity:
      “socialmedia”, “social_media”, “socmed”
      Lack of organisation:
      No links between tags, e.g. “SPARQL” and “RDF”
    • 82. Use case illustrating such issues
      Corporate use case > 3 years, 12257 tags, 21614 posts:
      54.2% of tags used once, 75.77% used <= 3 times
      Lots of valuable information lost in the long tail
      Tagging and expertise gap:
      194 items tagged with “TF” (= Thin Film)
      1% of them tagged with “solar”
      < 0.5% of “solar” items tagged “TF”
      Both tags are weakly related from a co-occurrence point of view, clustering cannot be efficiently used
    • 83. The long tail of tags
    • 84.
    • 85. The Tag Ontology
      The “Tag Ontology” by Newman from 2005:
      Based on Gruber’s tag model
      tags:Tag rdfs:subClassOf skos:Concept
      A “Tagging” class describing relationships between:
      A user
      An annotated resource
      Some tags
    • 86. SCOT
      SCOT (Social Semantic Cloud of Tags):
      A model to describe tagclouds (tags and co-occurrence)
      Ability to move your own tagcloud from one service to another
      Share tagclouds between services, and between users
      “Tag portability”
    • 87. MOAT
      MOAT (Meaning Of A Tag):
      A model to define “meanings” of tags
      e.g. SPARQL ->
      User-driven interlinking
      Tagged content enters the “Linked Data” web
      Collaborative approach to share meanings in a community
    • 88. MOAT with DBpedia example data
    • 89. Tagging process with MOAT and DBpedia
    • 90. MOAT in Drupal
    • 91. CommonTag
      A joint effort by AdaptiveBlue, DERI at NUI Galway, Faviki, Freebase, Yahoo!, Zemanta and Zigtag
      Linking tags to meaningful resource (à la MOAT)
    • 92. Life cycle for CommonTag data
    • 93. NiceTag
      NiceTag Ontology:
      Tagging meets speech act theory
      Focus on the link between a tag and a tagged item
    • 94. Extracting ontologies from tags
      Semi-assisted extraction of relationships between tags
      FoLksonomy Ontology enRichment
      Automated approach to identify tag meanings
      Can be combined with the previous models for a complete semantic tagging stack
    • 95.
    • 96. Mining hierarchical relationships from co-occurrence of tags by Halpin et al.
    • 97. LODr: semantic tagging for social data
    • 98. Faviki: bookmarking meets DBpedia
    • 99. Unifying conversations
      Some more semantically-enhanced systems
    • 100. Linking IRC to the Web of Data
    • 101. Mailing lists
    • 102. 102
      Bulletin boards
    • 103. SMOB
    • 104. Distributedarch
    • 105. An ontology stack for microblogging
      Combining the previous vocabularies for a complete representation of microblogging and microblogging activities
      Each microblog post is available in RDF (RDFa + raw RDF) on the publisher’s hub, using these models
    • 106. Semantic #tagging
      User-driven interlinking
      Real-time URIs are suggested when writing content
      Added ability to add new webservices (e.g. enterprise microblogging)
    • 107. Semantic microblogging mashups
    • 108. SPARQLing Social Semantic Web data
      Find all posts and their titles by John, using SELECT, and combining vocabularies (DC, SIOC, SIOC Types):
      SELECT ?post ?title
      WHERE {
      ?post rdf:type sioct:BlogPost ;
      dc:title ?title ;
      sioc:has_creator <$johns_URI> .
    • 109. SPARQLing Social Semantic Web data (2)
      Find all users that posted replies to John’s blog since January 2008, introducing the FILTER clause:
      SELECT ?who
      WHERE {
      ?post rdf:type sioct:BlogPost ; dc:title ?title ;
      sioc:has_creator <$johns_URI> .
      ?post sioc:has_reply ?reply .
      ?reply sioc:has_creator ?who ;
      dcterms:created ?date .
      FILTER (?date > "2008-01-01T00:00:00Z"^^xsd:dateTime)
    • 110. SPARQLing Social Semantic Web data (3)
      Find all content created by someone with a given OpenID URL:
      Browse someone’s social media contributions posted on various websites using different account names, but for the same person
      SELECT ?item
      WHERE {
      ?person foaf:openid <$openid> ;
      foaf:holdsAccount ?user .
      ?user sioc:creator_of ?item .
    • 111. Parse SPARQL results
      Many extensions (e.g. PHP5)
      Many examples
    • 112. Querying RDF files
      Bindings: Available for PHP, Python, etc.
      Example in Python:
      Import RDFm = RDF.Model()m.load(‘’)q = RDF.Query("SELECT ?s WHERE { ?s ?p ?o .}")results = q1.execute(model)for result in results: print result[’s']
    • 113. Need more data?
      Translate any data to SIOC:
      Re-use SIOC tools for non-SIOC data
      Semantic Pipes:
      SPARQL constructs:
      The “XSLT” of RDF; translate a set of RDF data from one graph format to another
      CONSTRUCT { ?x a sioc:Post . ?xsioc:has_creator ?y }
      WHERE { ?x a myont:BlogElement . ?xmyont:created_by ?y }
    • 114. From data to knowledge
      Semantic wikis
    • 115. Issues with traditional wikis
      Structured access
      Information reuse
      Made for humans, not machines
      He is the author of PelicanBrief.He lives in Mississippi.
      He writes a book each year.
      He is published by RandomHouse.
      Structured access:
      • Other books by JohnGrisham (navigation)
      • 116. All authors that live in Europe? (query)
      Information reuse:
      • The authors from RandomHouse (views)
      • 117. And what if I don't speak English? (translation)
    • Semantic wikis
      Capture some information about the pages in a formal language, letting machines process and reason on it:
      Some systems focus on metadata about the content, some on the social aspect, some on both
      A semantic wiki should be able to capture that an article about SPARQL is related to the Semantic Web and present you with further related information
      Various use cases and prototypes:
    • 118. From wikis to semantic wikis
    • 119. Structure / content
    • 120. SemperWiki
    • 121. Semantic MediaWiki
      An extension of MediaWiki, allowing users to add structured information to pages:
      Classifying links, e.g. making a relationship such as “capital of” between Berlin and Germany explicit:
      ... [[capital of::Germany]] ... resulting in the semantic statement "Berlin" "capital of" "Germany"
      Defining assertions:
      ... the population is [[population:=3,993,933]] ... resulting in the semantic statement "Berlin" "has population" "3993933"
      Currently the most widely-deployed semantic wiki
    • 122. Input using Semantic MediaWiki
    • 123. One possible output from a SMW query
    • 124. IkeWiki
    • 125. UfoWiki
    • 126. FromWikipedia…
    • 127. …to DBpedia
      @@ TODO
    • 128. DBpedia mobile
    • 129. Semantic social networks
      Using semantics in the analysis of social networks and social websites
    • 130. SNA with semantics
      Combining ontologies, folksonomies and SNA:
      Mika, “Ontologies Are Us”, ISWC 2005
      Ontology and SPARQL extensions for common SNA patterns:
      Ereteo et al., ISWC 2009
      SPARQL extensions (most are now in SPARQL 1.1):
      San Martin et al., ESWC 2009
    • 131. use case
      10 years of conversations, 150k users, 7M posts:
      Analysing the structured data that people link to
      To appear in Kinsella et al., i-Semantics 2010
    • 132. From raw data to rich data
    • 133.
    • 134. Some of the main sources of structured data
    • 135. New possibilities for SNA and SMA
    • 136. Semantic Enterprise 2.0
      Enterprise 2.0 goes semantic
    • 137. Some serious applications for Web 2.0
      Web 2.0 in research environments:
      Using wikis for project proposals
      Scientific community blogging (e.g. Nature Network)
    • 138. Enterprise 2.0
      Web 2.0 includes applications such as blogs, wikis, RSS feeds and social networking, while Enterprise 2.0 is the packaging of those technologies in both corporate IT and workplace environments:
      Corporate blogging, wikis, microblogging
      Social networking within organisations, etc.
      “Enterprise 2.0 is the use of emergent social software platforms within companies, or between companies and their partners or customers” - McAfee, MIT Sloan, 2006
    • 139. Enterprise 2.0 and the Web
      Many enterprises have an online presence on various Web 2.0 services to reach their customers:
    • 140. The SLATES acronym
      Search: Easy and relevant access to information
      Links: Enable better browsing capabilities between content
      Authoring: Easy interfaces to produce content, in a collaborative way
      Tagging: User-generated classification, enables serendipity and knowledge discovery
      Extension: Recommendation of relevant content
      Signals: Identify relevant content
    • 141. Social aspects of Enterprise 2.0
      Enterprise 2.0 introduces new paradigms in organisations with regards to knowledge sharing and communication patterns:
      Enterprise 2.0 is a philosophy
      Enterprise 2.0’s success depends on a company’s background:
      A study by AIIM showed that 41% of companies do not have a clear understanding of what Enterprise 2.0 is, while this percentage goes down to 15% in KM-oriented companies.
    • 142. Keys to Enterprise 2.0 adoption
      Combining top-down and bottom-up approaches helps to realise Enterprise 2.0:
      Top-down: Hierarchy (bosses!) sets up new tools and requires that various sections use them
      Bottom-up: Users become evangelists and word-of-mouth improves the number of new users:
    • 143. Business metrics for Enterprise 2.0
      13% of the Fortune 500 companies have a public blog maintained by their employees
      Forrester Research predicts a global market for Enterprise 2.0 solutions of 4.6 billion dollars by 2013, and according to Gartner, more social computing platforms will be adopted by companies in next 10 years
      Lots of companies and products in this space:
      Awareness, Mentor Scout, SelectMinds, introNetworks, Jive Software, Visible Path, Web Crossing, SocialText, etc.
    • 144. Open-source applications
      Open-source Web 2.0 apps can be efficiently used in organisations to build Enterprise 2.0 ecosystems:
      Blogging: WordPress, etc.
      Wikis: MediaWiki, MoinMoin, etc.
      RSS readers and APIs: MagpieRSS, etc.
      Integrated CMSs: Drupal, etc.
    • 145. Information fragmentation issues
      Heterogeneity of people, services, needs and practices leads to various services and tools being deployed
      By using various services (blogs, wikis, etc.), information about a particular object (e.g. a project) is fragmented over a company’s network:
      Getting a global picture is difficult
      Applications act as independent data silos, with different APIs, different data formats, etc.:
      Data integration can be a costly task
    • 146. Lack of machine-readable data and tagging issues
      Enterprise 2.0 enables and encourages people to provide valuable content inside organisations:
      However, information is complex to re-use, generally remains locked inside services, and is for human-consumption only
      Some queries cannot be answered automatically:
      “List all the US-based companies involved in sustainable energies”
      Plus there’s the aforementioned issue with tagging
    • 147. Semantic Web in enterprises
      Semantic Web technologies are already widely used in organisations:
      Ontology-based information management
      Semantic middleware between databases
      Intelligent portals
      Semantic Web Education and Outreach (W3C):
      NASA, Eli Lilly, Oracle, Yahoo!, Sun, etc.
    • 148. A Semantic Enterprise 2.0 architecture
      Lightweight add-ons to existing applications to provide RDF data:
      Exporters, wrappers, dedicated scripts, etc.
      Taking into account the social aspect (e.g. semantic wikis)
      Models to give meaning to this RDF data:
      Domain ontologies, taxonomies, etc.
      Applications on the top of it:
      Thanks to RDF(S)/OWL and SPARQL
    • 149. The RDF Bus approach
      RDF Bus architecture (Tim Berners-Lee):
      Add-ons to produce RDF data from existing Web 2.0 applications
      Store distributed data using RDF stores
      Create new applications:
      Semantic mashups
      Semantic search
      Open architecture thanks to a SPARQL endpoint, services as plugins to the architecture
    • 150. Relational DB to RDF mapping
      Relational data (RDB) is structured data and can be mapped to RDF straightforward:
      Allows integration of existing enterprise databases into the Semantic Enterprise 2.0 architecture
      Main issues include: closed-world vs. open-world modeling; assigning URIs for entities (records); mapping language expressivity
      For a state-of-the-art see
    • 151. LOD and Semantic Enterprise 2.0
      Huge potential for internal IT infrastructures to enhance existing applications (mashups, extended UIs, etc.):
      Integration of open and structured data from various sources at minor cost
      Issue: dependance on external services, replication may be required
      RSS is already widely used in organisations as a way to get information from the Web, LOD provides structured data to extend IT ecosystems
    • 152. Reusing LOD example (BBC Music Beta)
    • 153. Semantic Enterprise 2.0 use cases
      Electricité De France R&D:
      Integration of Enterprise 2.0 components using lightweight semantics
      Ecospace EU project:
      Interoperability of collaborative work environments
      European Space Agency:
      Integration of document repositories, databases and intranet data
    • 154. Use case: EDF R&D
    • 155. Use case: CWE interoperability
      private folders
      BC semantic folder
      BSCW shadow folder
    • 156. Use case: European Space Agency
    • 157. Recent developments
      Facebook Open Graph, Twitter Annotations, etc.
    • 158. Facebook Open Graph
      Allows metadata from external pages to be embedded (and claimed) within Facebook
      e.g. metadata about a restaurant (name, location, contacts) could be imported into a Facebook news feed via a “Like” button
      Good for Facebook, good for the Semantic Web?
      Yes, for both!
    • 159. A sample thing described using the OGP
    • 160. How we could link Open Graph things to blog posts / reviews
    • 161. OGP RDF schema (FOAF, DC, SIOC, GR)
    • 162. Twitter Annotations
      A forthcoming initiative by Twitter whereby it will be possible to attach arbitrary metadata to any tweet:
      Subject to an overall limit for the metadata payload
      May be possible to attach RDF-type statements
      Going beyond annotating tweets with geotemporal information:
      Allowing new types and properties for tweets
    • 163. What if your car could tweet?
      image from
    • 164. Diaspora effort and
    • 165. OneSocialWeb
    • 166. Appleseed project
    • 167. 166
      Lots more efforts……but not joined up!
      Social Graph API
    • 168. Everywhere real-time streams
      image from
    • 169. Some conclusions
      We’re not there yet, but we’re getting there…
    • 170. This area is hot right now
      image from
    • 171. 170
      A vocabulary onion, building on FOAF, SKOS, SIOC, SIOC Types, DC
    • 172. 171
      Disconnected sites on the Social Web / Web 2.0 can be linked using Semantic Web vocabularies
    • 173.
    • 174.
    • 175. 174
      Object-centred sociality refers to how we really use social websites:
      Can use semantics to describe this usage, by representing objects for linkage and reuse
      • Describe people, networks, content, presence, knowledge, tags, etc. with semantics
      Interlinking disconnected sites and profiles:
      Leveraging a “vocabulary onion” of ontologies
      Providing solutions for novel uses in organisations:
      Not just for the “Social” Web, but for Enterprise 2.0
    • 176. image from
    • 177. …now at
      Our new book…
    • 178. References
    • 179. Acknowledgements
      We thank our funding agency, Science Foundation Ireland, and also our colleagues:
      Uldis Bojars (SIOC)
      Sheila Kinsella (Semantic SNA)
      Milan Stankovic (OPO)