1. 1. Digital Library Applications of Social Networking
   Dr. Myungdae Cho
   Library School
   SungKyunKwan University
   myungdae.cho@gmail.com
   
2. 2. Agenda
   
3. 3. 1. Social Networking & Information Fluency
   From PIM (Personal Information Management) level
   - Is Memex incarnated?
   - more than hyperlink
   To Sociality by Link and Tags
   -> Inter-subjectivity
   - a thought in a user links to many thoughts in
   internet community
   ->Principle of Emergence
   
4. 4. Socialty
   (Messaging,
   Blogging,
   Streaming media)
   
5. 5. Machine, Humanand Socialty in Information Discovery
   Forms Inter-subjectivity
   Ontology (gives Subjective Path)
   In other words: Top down + Bottom up
   
6. 6. Another view of Machine, Humanand Socialty in Information Discovery
   RDF vocabularies
   or
   (Ontology)
   User-Created Metadata
   Linked Data
   (semantically organizeddata)
   Mapped data from existing DB (such as MARC)
   
7. 7. 2. Social Networking in Libraries
   Social networking could enable librarians and
   patrons not only to interact, but to share and
   changeresources dynamically in an electronic
   medium.
   
8. 8. Why do libraries care about social networking sites?
   The next big thing after Google is Social Networking.
   ( From As facebook takes off, Myspace strikes
   back Kirkpatrick, Davis. Foutune. Sept. 19, 2007)
   
9. 9. 2.1 Existing Library Application of Social Networking
   Librarything in libraries
   Delicious in libraries
   Mashup.. couldbe as an application of Linked data
   
10. 10. 2.1.1 Librarything in libraries
    http://www.librarything.com/
    Personalized desire from individuals needs
    Cataloguing thru Social Networking
    LibraryThing is a prominent social cataloging
    web application for storing and sharing personal library catalogs and book lists.
    
11. 11. Librarything in libraries
    LibraryThing helps you create a library-quality
    catalog of your books.
    LibraryThing connects people based on the books
    they share.
    
12. 12. 2.1.2. Delicious in libraries
    
13. 13. del.icio.us-like PennTags
    PennTags - When card catalogs meet tags
    http://tags.library.upenn.edu/
    http://www.slideshare.net/laurie.allen/penntags-presentation-at-educause-2006
    http://www.diglib.org/forums/fall2006/presentations/winkler-2006-11.pdf
    
14. 14. 2.1.3 Mash up in libraries
    http://library20.ning.com/profiles/blogs/mashup-your-librarys-twitter
    Mashup your Library's Twitter, Flickr, Youtube,
    Facebook accounts!
    
    • Libraries mash up content, services and ideas
    http://www.oclc.org/fr/fr/nextspace/009/1.htm
    Meebo Instant Messaging. Library Lookup.
    Bookburro.
    
15. 15. 3. how to lift existing metadata into a semantic level
    Mapping (Marc21 -> DC, Marc -> FRBR etc)
    Open Sources (Open Api)
    Linked Data
    
16. 16. 3.1 Open Source
    Open Source Social Platforms: 10 of the Best
    10 open source software platforms
    http://mashable.com/2007/07/25/open-source-social-platforms/
    www.programmableweb.com
    SungKyunKwan University: Use ofOpen API http://lib.skku.edu/index.ax
    
17. 17. 3.2 Linked Data
    Oh my goodness, the original web of documents was just the tip of iceberg.
    ( Sir Tim Berners Lee, July 2008)
    
18. 18. What is it?
    
19. 19. Closed containers of data
    Information systems, such as library catalogs, have
    been, and still are, for the greatest part closed
    containers of data, or silos without connections
    between them, inaccessible to Web architecture (No Url, no links) with a few exception.
    (Tim Berners Lee)
    free from the capsules of the catalog
    
20. 20. Linked Data
    Linked Data is a methodology for providing
    meanings and relationships between things anywhere on the web, using
    URISfor identifying,
    RDF for describingand
    HTTP for publishing
    
21. 21. Two valuable notions from library community
    Collocations
    
    • 1876 / Charles Cutter
    • 22. Resources with the same or related content are grouped together.
    Disambiguation
    
    • clarification that follows from the
    removal of ambiguity
    
22. 23. Collocations through Linked Data
    Wiki: http://www.wikipedia.org/
    vs
    dbpedia : http://dbpedia.org/About
    WorldCat: http://www.worldcat.org/
    vs
    Fictionfinder (FRBR model): http://fictionfinder.oclc.org
    
23. 24. rdf identifiers as a disambiguation
    http://rdf.freebase.com/?freebaseid
    http://rdf.freebase.com/ns/en.blade_runner
    
24. 25. rdf identifiers as a disambiguation
    annotation
    Disambiguation process
    
25. 26. rdf identifiers as a disambiguation
    
26. 27. Another disambiguation_dereferenceable URIs
    
27. 28. In summary so far:Paradigm Shift in www
    
28. 29. 4. Librarys role in Semantic Web
    Phase 1: Semantifying MARC, Thesaurus etc
    Translating LC controlled vocabularies and authority control for named entities, thesauri from domain specific societies and institutions into RDF/RDFS, OWL, SKOS with URIs assigned according to Linked Data Design Principles (TBL, 2007)
    Phase 2:
    Authority data discovery, sharing, and reuse, e.g., LC authorities & Vocabularies, OCLCs Faceted Application of Subject Terminology (FAST) etc
    Phase 3: Into the Semantic Web
    Web of Linked data
    DBPedia
    GeoNames
    Librarything
    
29. 30. Case: OCLC Semantic Web Projects
    
    • FRBRising projects
    • 31. Developed FRBR work set algorithms andxISBN Web Services
    • 32. FictionFinderhttp://fictionfinder.oclc.org
    • 33. WorldCat Identifiers (20 million identifiers)
30. CV: Why establish controlled vocabularies?
    Control values that occur in metadata
    Reduce ambiguity
    Control synonyms
    Make documentation available for reuse
    validate terms (by subject heading /LCSH)
    Establish formal relationships among values where appropriate
    Controlled vocabularies: ALA program on Linked Data
    ALA Annual 2009
    
31. 34. Types of Controlled Vocabularies used in metadata standards
    Lists of enumerated values
    Code lists (e.g. language, country)
    Taxonomies
    Formal Thesauri
    Locally controlled enumerated lists
    Controlled vocabularies: ALA program on Linked Data
    ALA Annual 2009
    
32. 35. Thesauri
    A thesaurus is a controlled vocabulary with multiple types of relationships
    Example:
    Rice
    UF Paddy
    BT Cereals
    BT Plant products
    NT Brown rice
    RT Rice straw
    
33. 36. Standards maintained at LC contain controlled vocabularies
    LCSH/NAF
    Thesaurus of Graphic Materials
    ISO 639-2 (language codes)
    MARC (including code lists)
    MODS
    METS
    PREMIS
    MIX (XML schema for NISO Z39.87 Technical metadata for digital still images)
    and some others
    
34. 37. Representing information about controlled vocabulary values
    Data elements in metadata formats, e.g. MARC Authority format
    XML schemas (sometimes as enumeration values)
    RDF/XML and RDFS (Resource Description Framework)
    SKOS
    MADS (Metadata Authority Description Schema)
    
35. 38. Reasons for developing a web service for vocabularies
    Facilitate development and maintenance process for vocabularies
    Make controlled lists openly available
    Provide comprehensive information about controlled values
    Experiment with semantic web technologies and linked data
    Expose vocabularies to wider communities
    
36. 39. Popular Rdf Vocabularies
    People + Organisations
    FOAF, HCard, Relationship, Resume
    Places
    Geonames, Geo
    Events
    RDFCalendar
    Social Media
    SIOC, Review
    Topics + Tags
    SKOS, MOAT, HolyGoat
    eCommerce
    GoodRelations, CC Licensing
    More...
    Scovo, DOAP, Recipes, Measurements, ...
    
37. 40. SKOS
    Simple Knowledge Organisation System(s)
    A Semantic Web standard called Simple Knowledge Organization System (SKOS) defines the organization of terms into thesaurus form, with broader and narrower terms and alternate terms including alternate language entries
    Simple, extensible, machine-understandable representation for concept schemes
    Thesauri
    Classification Schemes
    Taxonomies
    Subject Headings
    Other types of controlled vocabulary
    Disadvantage: unusual concept schemes dont fit into SKOS (original structure too complex)
    
38. 41. A Method to Convert Thesauri to SKOS
    Case1
    Original XML data file:http://www.esd.org.uk/standards/ipsv/ipsv.xml
    Original XML Schema filehttp://www.esd.org.uk/standards/xmlschemas/taxonomy-v3.0.xsd
    Conversion program:convertipsv.pl(contains instructions for usage)
    Resulting RDF:ipsv/rdf/ipsv.rdf
    SKOS Core schema:http://www.w3.org/2004/02/skos/core/history/2005-10-14(version used for this paper, for latest version seehere)
    Additional IPSV schema:ipsv/ipsv1-eswc06.rdf
    Case 2
    Partial original data files:gtaa/SampleOfGTAA.zip
    Conversion program:gtaa/GTAAtoSKOSinstanceRDFSv6.pl
    Resulting RDF:gtaa/GTAAinstancesSKOSv7.rdf
    SKOS Core schema:http://www.w3.org/2004/02/skos/core/history/2005-10-14(version used for this paper, for latest version seehere)
    Additional GTAA schema:gtaa/GTAAskosModelRDFSv4.rdfs
    
39. 42. Converting into SKOS graph
    Identify
    Describe
    Publish
    
40. 43. Identify
    Step 1: Identify concepts
    http://www.example.com/concepts#lovehttp://www.example.com/concepts#awehttp://www.example.com/concepts#joy
    
41. 44. Describe
    Step 2: Describe
    
42. 45. Publish
    Step 3: Publish
    Put the file on a web server for programs to download & process
    Put the file on special RDF server on which you can query with SQL-like language:
    Select * from Where
    
43. 46. Publishing LCSH in the Web
    Project LCSH into RDF (i.e., create an RDF representation)
    Library of Congress Subject Headingsavailable aslinked-datausing theSKOSvocabulary. http://lcsh.info
    
44. 47. LCSH in SKOS
    
    
45. 48. LCSH in SKOS
    
46. 49. LCSH in SKOS
    
47. 50. Skosuse cases_2: NSDL Metadata Registry
    http://metadataregistry.org/
    
48. 51. Skosuse cases_3: getty
    http://www.getty.edu/research/conducting_research/vocabularies/download.html
    
49. 52. Skosuse cases_4: Agricultural Information Management Standards (AIMS)
    http://aims.fao.org/en/search/google/cow?query=cow&cx=011162950886884224513:ennli7xeebg&cof=FORID:11&sitesearch=&hl=en&ie=utf-8&oe=utf-8&lr=lang_en
    
50. 53. FRBR conceptual model
    Coyle (2008) advocates FRBR conceptual model
    as part of a semantic model in saying
    Since FRBR is about entities and relationships, it seems to be perfectly positioned as the first step in the transformation of library data to the
    semantic web.
    
51. 54. FRBR
    Expression of Core FRBR Concepts in RDF
    http://vocab.org/frbr/core.html
    This vocabulary is an expression in RDF of the
    concepts and relations described in the IFLA
    report on the Functional Requirements for
    Bibliographic Records (FRBR).
    
52. 55. FRBR as a RDF vocabulary
    FRBR is a complete data model that is a new way of looking at our data, not just taking existing records and identifying work relationships.
    FRBR a type of RDF vocabulary
    entities and the relationships inFRBR is identifiable,
    linkable, usable, and reusable, and everything can
    be matched up.
    
53. 56. FRBR-SKOS
    
54. 57. RDA (Resource Description and Access)
    The new cataloging rules, replacing AACR2
    RDA -> RDF
    Joint DCMI/RDA task force
    Seed funding to develop initial prototype RDF vocabularies for bibliographic information
    Based on FRBR and data model implicit in RDA
    Early stage year
    http://dublincore.org/dcmirdataskgroup/
    Karen Coyle
    
55. 58. library related Linked Data projects
    A brief and incomplete list of some library related
    Linked Data projects:
    RDF BookMashup Integration of Web 2.0 data sources like Amazon, Google or Yahoo into the Semantic Web.
    Library of Congress Authorities Exposing LoCAutorities and Vocabularies to the web using URIs
    DBPedia Exposing structured data from WikiPedia to the web
    LIBRIS Linked Data interface to Swedish LIBRIS Union catalog
    Scriblio+Wordpress+Triplify A social, semantic OPAC Union Catalogue
    
56. 59. Language of Interoperability
    Universal identifiers (URIs): like written word
    For connecting the dots
    Abstract syntax (RDF triples): sentence grammar
    Foundation of syntactic interoperability
    Vocabularies: words and concepts
    Foundation of semantic interoperability
    Platform for compatible domain models
    Application Profiles
    Human-understandable machine-processable
    
57. 60. 5. Proposed Models for Libraries with Linked data
    A publisher provides basic information about a book
    (e.g., using onix)
    The National Library adds bibliographic and authority
    control
    A local library adds holding information
    Some nice guy out there adds links from, say, Wikipedia
    A librarys IT staff creates a Webpage where I can find all related information regarding this book, links to related books from the same author, on the same subject, the authors bio from wikipedia, comments from other Portals.
    =>Since, instead of following links between HTML pages, Linked Data browsers enable users to navigate between different data
    sources by following RDF links.
    How about User-created metadata
    
58. 61. Advantages over other methods
    No crosswalk/mapping
    - Each one uses his own metadata format, all triples can be aggregated
    No data redundancy
    - Each one creates only the data he needs, and retrieves already existing information
    No harvesting
    - The data is available directly on the Web
    No branding issue
    - The URIs allow to track down the original datawhatever its origin
    No software-specific developments
    - Everything relies on open standards as RDF, SPARQL no need to learn a new protocol or query
    language
    
59. 62. Thank you for listening!
    Questions?