The document discusses the use of semantic web technologies for representing and utilizing folksonomies, focusing on the integration of tags with the semantic web. It outlines various methodologies, including the 'fast unfolding of communities in large networks' algorithm, to analyze community structures and enhance data representation. Future enhancements aim to expand the application to larger tag models and optimize the processes for querying and writing data.

1. Exploiting Semantic Web Techniques for Representing and Utilising FolksonomiesOwen Sacco

2. page 1Presentation Map

3. page 2Presentation MapIntroductionAim & GoalsThe Semantic WebMeta Formats, Vocabularies & Query LanguageWeb 2.0Web 2.0 Technologies & ApplicationsFolksonomiesTags, Tagging, Representing Tags Semantically & Integrating Folksonomies with the Semantic Web

4. Presentation MapGraph Mining TechniquesFast Unfolding of Communities in Large NetworksState of the Art ToolExamining the Edge ListThe Community Structure OntologyJena & CoreseCreating & Querying RDF StatementsAnalysis & ResultsConclusionEnhancements & Future Workpage 3

5. Introductionpage 4

6. IntroductionThe research is about:Understanding various Semantic Web technologies for representing data semanticallyUnderstanding Folksonomies and how to semantically represent themTo semantically represent tags retrieved from Bibsonomy (http://www.bibsonomy.org/) The tags have been hierarchically structured using the algorithm “fast unfolding of communities in large networks”Use Semantic Web technologies to create and exploit such representation of tagspage 5

7. The Semantic Webpage 6

8. The Semantic Webpage 7What is the Semantic Web?Not a separate Web An extension of the current WebSemantic = MeaningSemantic Web = Meaningful DataMeaning is data about data, i.e. MetadataAdvantages of Semantic Web:Information is given well-defined meaning Better enabling computersPeople to work in cooperation Source: W3C Semantic Web

9. The Semantic WebResource Description Framework (RDF)A framework that describes resources on the WWWSuitable for merging data on the WebResources are uniquely identified by URLsThe RDF Model is made up of triple statementsTriple Statements: Subject, Predicate & Objectpage 8PREDICATESUBJECTOBJECT

10. The Semantic WebAn RDF Model can be serialised in RDF/XMLAn example of RDF document<?xml version="1.0"?> <rdf:RDFxmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:contact="http://www.w3.org/2000/10/swap/pim/contact#"> <contact:Personrdf:about="http://www.w3.org/People/EM/contact#me"> <contact:fullName>Eric Miller</contact:fullName> <contact:mailboxrdf:resource="mailto:em@w3.org"/> <contact:personalTitle>Dr.</contact:personalTitle> </contact:Person> </rdf:RDF> Source: W3C RDF Primerpage 9

11. The Semantic WebOntology“A formal explicit specification of a shared conceptualisation”In other words: parties having a common concept of data agree and specify clearly as possible such conceptsIt is an enabling technology for information sharing and manipulationA vocabulary for RDF documentsOntologies are based on RDF models and are expressed by using the Web Ontology Language page 10

12. The Semantic WebSPARQL – An RDF Query LanguageQuery in the Semantic Web context means: “Technologies and protocols that programmatically retrieve information from the Web of Data”.Based on triple patterns similar to RDF triplesA query returns resources for all RDF triples that match the query’s patternIs used to return complex data for mash-ups or search engines containing semantic dataSyntax is similar to SQLSource: W3Cpage 11

13. Web 2.0page 12

14. Web 2.0A “Read/Write” WebWeb 2.0 has:Facilitated web designProvided attractive, rich, easy-to-use interfacesAssisted in reuse of data by merging information from various sourcesCreated social networks of peopleAccording to Internet World Stats, between 2000 and 2003 users doubled thanks to Friendster (one of the first social network websites)Source: Internet World Stats - Internet Growth Statisticspage 13

15. Web 2.0Web 2.0 is considered a Social WebPeople are more involved by collaborating & sharing dataOne of the major Web 2.0 technologies for web development is AJAXA combination of several technologies:HTML or XHTMLCascading Style Sheets (CSS)Java ScriptXMLpage 14

16. Web 2.0Web 2.0 created new application concepts:Blogs (Blogger, WordPress)Wikis (Wikipedia)Really Simple Syndication, RSSMashups (MusicMesh, BBC Music)Social Networks (Facebook, LinkedIn, MySpace)Social Bookmarking (delicious, Bibsonomy)Photo Sharing (Flickr)Video Sharing (YouTube, Vimeo)In most of these concepts you find Tagging!page 15

17. Folksonomiespage 16

18. FolksonomiesTag“A non-hierarchical keyword or term”Tagging“Assign a tag to a piece of information or resources”Tagger“The person that assigns the tag”Folksonomy“The result of personal free tagging of information and objects for one’s own retrieval. The tagging is done in a social environment.” Thomas Vander Wal (2004)page 17

19. FolksonomiesTag Clouda visualisation of popular tags popular tags stem out from others by being in larger font or emphasisedpage 18

20. Folksonomiespage 19Where can we tag?Social Bookmarking websites

21. FolksonomiesPicture sharing websitespage 20

22. FolksonomiesVideo sharing websitespage 21

23. FolksonomiesWhy tagging?It’s PopularNowadays, practically anyone who uses a computer or the Internet is exposed to tagging in some way.It’s SocialThrough the most popular tags, we can see a kind of rough consensus on the subject of the resource.It’s FlexibleAd-hoc, free-form and does not adhere to any strict classification scheme or vocabulary.page 22

24. FolksonomiesBasic ModelTaggers create the tags, and sometimes they add resources.If we can identify something, then it can be tagged.Tagging is open-ended, tags can be any kind of term.page 23Source: Smith G. 2008. Tagging People-Powered Metadata for the Social Web

25. FolksonomiesHow about:Collaborative sharing tags across multiple applicationsCollaborative filtering based on taggingConnecting people based on taggingAll these can be achieved through Tag OntologiesOntology is not a taxonomyOntology makes semantic agreementSemantic agreement enables useful compositionpage 24

26. FolksonomiesRichard Newman’s Tag Ontologypage 25Source: Haklae Kim et al., Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces

27. FolksonomiesTom Gruber’s Conceptual ModelTagging(object, tag, tagger, source, + or -)page 26Source: Gruber T., Ontology for Folksonomy: A Mash-Up of Apples and Oranges.

28. FolksonomiesLimitations of tagging:Ambiguity of tags (example: apple is it a fruit or the computer company?)Lack of synonymy (example: lorry or truck)Discrepancies in granularity (example: java vs programming language)Flat Organisation of FolksonomyHow do we overcome these?Use: CommonTag, MOAT, SCOTpage 27

29. FolksonomiesCommonTagTo add concepts to tags from databases such as Freebase and DPPedia page 28Source: CommonTag

30. FolksonomiesMeaning Of A Tag (MOAT)An ontology to represent how different meanings (URIs of semantic Web resources) can be related to a tagExtends the Tag class from Richard Newman’s tag ontologyTagging (User, Resource, Tag, Meaning)Architecture of MOAT Framework:MOAT server stores different meanings that can be queriedMOAT client interacts with the server to let users easily annotate their contentpage 29

31. FolksonomiesSocial Semantic Cloud of Tags (SCOT)An ontology aimed to represent set of tagsBuilt on top of Richard Newman’s Tag Ontologypage 30Source: SCOT: Let's Share Tags!

32. FolksonomiesLimitations of the previous ontologies:An extra step is being added to the tagging activityIsn’t it daunting for the user when presented with a list of meanings to choose from? Which meaning shall the user choose?Will tagging remain popular with this additional step?If an automatic process is used to select a meaning of a tag, how accurate can this process be? Can this process really understand the user at that instance? page 31

33. FolksonomiesWith this additional meaning, isn’t tagging becoming another “strict” classification scheme?Can relationships of tags really be built on meanings?How about using some form of algorithm that can unfold new relationships of tags?page 32

34. Fast Unfolding of Communities in Large Networks page 33

35. Fast Unfolding of Communities in Large NetworksA recursive method to extract the community structure of large networksThis method is based on modularity optimisationThe modularity is a scalar value that measures the density of links inside communities as compared to links between communitiesIt unfolds a complete hierarchical community structure for large networks in a short timeResults have shown that on a network of 118 million nodes, the algorithm took 152 minutespage 34Source: Blondel V.B. et al. 2008. Fast unfolding of communities in large networks

36. Fast Unfolding of Communities in Large NetworksThe algorithm consists of two phases which are iterated until a maximum modularity is attained.First, all nodes are assigned to different communities.Then each node is compared with its neighbours. The node is placed in the community which yields a maximum gain in modularity.This process is repeated for all nodes until no further movement can be attained.The second phase consists of building a network whose nodes are now the communities found during the first phase.page 35

37. Fast Unfolding of Communities in Large NetworksAfter the second phase, the process starts again with the first phaseA “pass” denotes a combination of both passesThe “passes” are iterated until there are no more changes and the maximum modularity is reached for the whole networkThe height of the network denotes in the number of passesAt the end, a hierarchical structure is attained that consists of communities of communities.page 36

38. Fast Unfolding of Communities in Large Networkspage 37

39. State of the Art Toolpage 38

40. State of the Art ToolThe DataIt is provided beforehandConsists of a hierarchical structure made up of communities of communities of related tags This hierarchical structure is constructed using the “Fast Unfolding of Communities in Large Networks” algorithmThe tags are from the Social Bookmarking Website Bibsonomy (http://www.bibsonomy.org/)The aim for using the community structure algorithm is to unfold new relationships amongst tagspage 39

41. State of the Art ToolA visualisation of tagging graph that depicts the relationships amongst tagspage 40

42. State of the Art ToolThe Input to the system will consist of Edge ListsEach Edge List file consists of a pass4 Edge List files were used for this system: The first list is a plain list of related tags queried from BibsonomyThe other three lists denote communities or communities of communities computed from the community structure algorithmEach relation (line) in each of the Edge List file consists as follows:The first edge list: <tagi, tagj, weight>page 41

43. State of the Art ToolThe other three edge lists:<communityi, tagj, weight> or <communityi, communityj, weight>The Edge List files contain:The first (lower level): 13126 nodes with 264718 edgesThe second (first pass): 529 nodes with 6337 edgesThe third (second pass): 65 nodes with 374 edgesThe fourth (third pass): 50 nodes with 207 edgespage 42

44. State of the Art ToolA sample from one of the edge lists (the lower level file)caching,offlinebrowser,2.0caching,archiving,2.0institutions,activity,1.0malian,senegal,2.0malian,northern,2.0malian,guinea,2.0malian,drummers,2.0cdf,c,1.0cdf,library,1.0page 43

45. State of the Art ToolFirst Task: To semantically represent all edge lists that represent the hierarchical structureSince the lower level edge list is made up of a set of tags, then the tags will be described using the SCOT ontologyBut to represent the hierarchical structure of communities, a new ontology must be designed that needs to be built on top of SCOT and also, the new ontology must be linked to SCOTpage 44

46. State of the Art ToolThe Community Structure Ontologypage 45CommunityStructureUnfoldedCommunityUnfoldingActivityCommunityCommunityAggregationlinkedInassociatedCommunitylinkedWithLinkagenamesioc:ResourcemodularitypasslinkedTagcommunityOfCommunitylinkWeightscot:Tag

47. State of the Art ToolOntology was designed with a tool called Protege – A Java application for designing OntolotgiesOntology built on OWL2Classes: CommunityStructure, Community, CommunityAggregation, LinkageObject properties: associatedCommunity, communityOf, linkedIn, linkedTag, linkedWith, unfoldedCommunity, unfoldingActivityData properties: communityName, linkWeight, modularity, passpage 46

48. State of the Art ToolSecond Task: To create an application that will transform the edge lists to RDF/XML statements and store the documents on physical storage. Also, a query engine will be included into the application to query the RDF/XML statements.The application is developed using the Java programming language.For the creation of RDF/XML statements and to write such statements to physical storage, a widely used API is embedded in the system. This API is called the JENA APIpage 47

49. State of the Art ToolJena – A Semantic Web FrameworkDeveloped by HPAn RDF API for reading and writing RDF models in RDF/XMLAn OWL API for reading and writing OWL ontologiesIn-memory and persistent storage for writing RDF/XML statements to memory or physical storage such as text files or even relational databasesSPARQL query enginepage 48

50. State of the Art ToolThe Toolpage 49

51. State of the Art ToolThe tool provides the following features:Properties to setup:The Edge List DirectoryThe Edge List File Structurepage 50

52. State of the Art ToolSettings to setup the type of storage requiredRDF/XML documentspage 51

53. State of the Art ToolRelational database persistent storageA TDB storage, a custom fast persistent storagepage 52

54. State of the Art ToolProperties to setup the Ontologiespage 53

55. State of the Art ToolThe Method to transform the edge list to RDF Statements:First, the edge lists are merged together and ordered according to their hierarchical structureSecond, the RDF Model consisting of RDF statements are created according to the Community Structure and SCOT OntologiesThird, RDF statements are written according to the settings setup.page 54

56. State of the Art ToolWriting of RDF StatementsRDF Documents:For whole documents: the whole document is written after the whole model is createdFor split documents: documents are written after the model for each community is created.Two index lists are created, one A-Z and an other to indicate where each community document is locatedpage 55

57. State of the Art ToolWriting of RDF StatementsRDF Persistent StorageRDB Method: MySQL is used as a persistent relational databases and RDF statements are written on-the-fly, i.e. After each statement is created, these are written in the databaseTDB Method: each statement is written on-the-fly as wellpage 56

58. State of the Art ToolWriting of RDF Statements (Results)page 57

59. State of the Art ToolQuerying StatementsFor RDF Documents Corese SPARQL Engine was used Corese SPARQL Engine is developed by EdelweissBuilt on top of Jena with some added enhancements such as Approximated Searches, Select ExpressionsQueries only RDF documents and does not have the capability of querying directly to relational databasespage 58

60. State of the Art ToolQuerying StatementsFor Persistent Storage, the Jena SPARQL Engine is used since Jena allows for direct queryingQuerying MethodsRDF Documents (Split Documents):First query index listsGet community documentQuery community document and get linked communitiesQuery index list and query contents for each communitypage 59

61. State of the Art ToolQuerying MethodsRDF Documents (Whole Documents)Query whole model and query for communityRetrieve linked communitiesQuery linked communities for their contentPersistent StorageQuery whole model and query for communityRetrieve linked communitiesQuery linked communities for their contentpage 60

62. State of the Art ToolQuerying Statements (Results)Results are based on a community called malianThis community has 57 linked communities and 15 linked tagspage 61

63. State of the Art ToolOther featuresRDF Document Viewerpage 62

64. Conclusionpage 63

65. ConclusionIn this research we have seen the importance of Semantic Web and to describe semantically Web dataWe have seen the importance of using folksonomies for search and exploration Additionally, we have also seen various ontologies of how such folksonomies can be semantically representedFrom community structure algorithms and graph mining techniques, new relationships amongst other tags can be unfoldedpage 64

66. ConclusionAn ontology was designed and developed for the fast unfolding of communities in large networksFrom this ontology, RDF/XML statements can be created and are linked to the SCOT ontologyWe have seen that by using Triple Stores, persistent storage for triple statements is much faster for queryingpage 65

67. Future Enhancementspage 66

68. Future EnhancementsTo try this model on larger tag models from different websitesTo include the tagger and links to the actual resourceTo analyse these links that contribute to the linked data initiativeOptimise writing and querying based on larger modelspage 67

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