Social semantic web


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Social semantic web

  1. 1. Social Semantic Web
  2. 2. Social Web• Term used to describe the way in which people socialize/interact on the web
  3. 3. Social web• =socializing sites + content sharing sites
  4. 4. Social Semantic Web• Interconnect the islands of the social web with semantic technologies• Enhance the semantic web applications with the wealth of knowledge from user generated content
  5. 5. What do people do on the social web• Create content
  6. 6. What do people do on the social web web• Tag content
  7. 7. What do people do on the social web• Create or state relationships
  8. 8. What do people do on the social web• Exchange messages• Search• Buy
  9. 9. Social web• Limitations – Social platforms are isolated from one another – There is no standard for exchanging data between them • Nobody wants to have a common standard for exchanging data :) – Database hugging (see TBL’s Ted talk from first course) <=> User base hugging
  10. 10. Social Web - content types• Videos (youtube, vimeo, google video, …)• Bookmarks (delicious, stumbleupon, digg)• Blog articles (wordpress, blogspot, technorati)• Microblog posts (twitter, facebook statuses)• Images (flickr, picasa)
  11. 11. Social Web - content types• Characteristics – Very different types – Most of them do not contain text – Bookmarks are references to the other types of resources – Important who creates or bookmarks the web resource – The only way to describe all these types of content => tags
  12. 12. Social web - tagging - Folksonomies• A folksonomy is “tagging that works”• Another definition: the result of free tagging of net objects, identified by URL’s. The set of tags and resources annotated by users constitutes the folksonomy.
  13. 13. Folksonomy characteristics• No hierarchy, no is-a relations• Vocabulary reflects the ideas and vision of the users• Terms in the folksonomy are related only through co-occurrence• Much more dynamic than ontologies to represent the way users categorize content
  14. 14. Types of folksonomies• Broad folksonomies – Many people tag the same object. More people can use the same tag on a resource – Ex:
  15. 15. Types of folksonomies• Narrow folksonomies – A resource is tagged by a limited number of people (usually the author) – Used more in content sharing sites (youtube, slideshare, flickr) for describing multimedia content – Less information than the broad folksonomies but also relevant
  16. 16. Folksonomies - problems• Different lexicalizations of words (plural, singular, synonyms)• Words with multiple meanings• There’s no hierarchy - problems for classification of resources
  17. 17. Folksonomies - representation• Hypergraph representation• Hypergraph=a generalization of a graph where an edge can connect any number of nodes.• a hypergraph H=(V,E) where V is a set of nodes and E is a set of non-empty sub-sets of V. E is a subset of the power set of V.
  18. 18. Folksonomies - representation• The nodes of a folksonomy hypergraph are: – The users that are tagging – The resources that are being tagged – The tags that are being used• The edges of the hypergraph are triples of type (user, resource, tag) which represent the tagging action performed by a user using a tag on a given resource.• This means that the edges in the hypergraph belong to a subset of the U X R X T product
  19. 19. Folksonomy - representation• => a folksonomy F=(U,R,T,Y) – where Y U X R X T.• Folksonomies can also be represented using ontologies• The hyperedge are represented through a node of type tagging and the node is linked through properties to the user, resource and tag
  20. 20. Vocabularies for the Social Web• Ontologies that describe the social web – Content (SIOC, DC) – Social relations (FOAF, Relationship, XFN) – Tagging (Tagging, SCOT, MOAT)
  21. 21. Social Web Vocabularies• SIOC - Semantically Interlinked Online Communities
  22. 22. Social Web Vocabularies• SIOC + FOAF + SKOS
  23. 23. Social Web Vocabularies• Relationship vocabulary -• Defines different kind of properties that describe the relations between two persons• Ex: acquaintance, ancestor, apprentice, child, close friend, collaborator, knows in passing, knows by reputation• Would be very useful in social networks where you have hundreds of “friends”
  24. 24. Social Web Vocabularies• XFN - XHTML Friends Network• Simple microformat for describing relationships
  25. 25. Social Web Vocabularies• Neuman’s tagging ontology -• Tagging - the hyperedge n-ary relation between a tag, a resource, a tagger and a date.• Tag - class to define the tags• Doesn’t express tag meanings or lexicalisations
  26. 26. Social Web Vocabularies• SCOT ontology – Extension of SIOC and Tagging (Neuman’s) – + concepts and properties • TagCloud (set of tags used in a context) • CoOccurrence • Source - the namespace where the tagging was performed
  27. 27. Social Web Vocabularies• MOAT - meaning of a tag• it introduces the concept of TagMeaning, linking a tag concept to a related concept in a domain ontology.•
  28. 28. Model to represent social web activity
  29. 29. Social Search• 3 types of social search – Collective social search – Friend filtered social search – Collaborative search (question-answering)
  30. 30. Social search• Collective social search – Similar with wisdom of the crowds – Search could be augmented with “hot” topics – Problems with trust - does the user really trust the results from the masses – Need to understand the user’s search process - is the user exploring or narrowing the domain
  31. 31. Social search• Friend-filtered social search – Provide data “validated” by your friends or your peers – Add it next to traditional search results – Advantage of TRUST – See who added or recommended the piece of content returned by the search
  32. 32. Social search• Friend filtered social search - issues – Do your friends have relevant and available content for most of your searches – Problems with the different types of social content - most of them only characterized by tags – Difficult to understand the context around a resource (the tagging context for example)
  33. 33. Social search• Collaborative Search - question answering – A number of users work together to find the answer to a problem – Example: yahoo answers (asynchronous) – Example: Aardvark - IM based - find the user from your network who might know the answer and dispatches the request
  34. 34. Social Buying Amazon sets the example as always Most read part of the product description -people reviews Just imagine having reviews from someone you already know and trust
  35. 35. Semantic Social Web Tools
  36. 36. Semantic social web - our vision• What we try to do – Provide feedback for learning using social web data – Provide friend-filtered social search – Provide relevant learning recommendations – Help the learner establish valuable network connections – Understand the learner’s profile – Our context: European research project - Language Technologies for Lifelong Learning
  37. 37. Semantic social web - our vision• Friend filtered social search - the “friends” are the tutors and learning peers• The tutor is (should be) well connected, the learner might get introduced to valuable persons• The learner should TRUST the results because they come from tutors• If the tutor invests time in adding resources to the social web - he gains time afterward because he has to answer less questions
  38. 38. Semantic social web - our vision• Data sources – - bookmarks – - presentations – - images – - videos• To be added: – Twitter – Facebook – Blogs
  39. 39. Semantic social web - our vision