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Linked Services for the
Web of Data

John Domingue, Knowledge Media Institute,
The Open University, UK
STI International, Austria
Overview
• Linked data introduction
  – Linked data successes
• Linked Services
  – Approaches and principles
  – Technologies supporting Linked Services
     • Models: MicroWSMO, WSMO-Lite and the Minimal Service Model
     • Tools: iServe, SWEET and OmniVoke
• Sample applications
  – House hunting
  – Integrating advertising and video in Watch‟n‟Buy
• Current and future work
• Summary
LINKED DATA INTRODUCTION
Semantic
Web
Stack
RDF = Subject, Property, Value
Triples
Triples combine to make Graphs
Linked Data Principles
 Set of best practices for publishing structured
 data on the Web in accordance with the general
 architecture of the Web.



 1. Use URIs as names for things.
 2. Use HTTP URIs so that people can look up those
    names.
 3. When someone looks up a URI, provide useful
    RDF information.
 4. Include RDF statements that link to other URIs so
    that they can discover related things.

 Tim Berners-Lee, http://www.w3.org/DesignIssues/LinkedData.html, 2006
LINKED DATA SUCCESSES
BBC Sports
BBC
Sports
BBC
Sports
www.bbc.co.uk/ontologies/sport
Live Video Streams
I „Like‟ Casablanca
People, photos, friends and the
Web
Google Buys MetaWeb
Freebase Data
Application Portals
Where does my money go?
ASBOrometer
Linked Open Data Cloud
LINKED SERVICES
Linked Data and Services
• Provide a platform for building applications on top of
  Linked Data
• Connect services and semantic formats within the Web
  context
• Ease the tasks associated with building applications
  from online service components
Web APIs




J. Mosser: “Open APIs: state of the market”, Glue conference 2011
LINKED SERVICES APPROACH
AND PRINCIPLES
Linked Services Principles
• Services described as Linked Data
  – Inputs, outputs, functionality, etc is described using RDF(S) and
    using existing vocabularies
• Consume and produce RDF
  – Applications may contain „standard services‟ too
• Process layer on top of the Web of Data
TECHNOLOGIES SUPPORTING
LINKED SERVICES
MicroWSMO & WSMO-Lite




                        33
Minimal Service Model
LPML         Analysis &
SWEET & SOWER                          deployment
                          Discovery
                                                      Monitoring Invocation


                         Process Editor



                                                    incl. BPEL-based execution
                                                            environment
                      incl. TG, Optimizer, DTC


                            Process
                            modeling
           Service                                   Process
         annotation         SOA4All                 execution
                        Process Lifecycle
ISERVE SERVICE REPOSITORY
iServe Key Features
 • Support for several SWS formalisms
   – WSMO-Lite, MicroWSMO, SAWSDL, OWL-S
 • Supports access via
   –   Web Application - iServe Browser
   –   Read and Write RESTful API
   –   Linked Data principles
   –   SPARQL endpoint
   –   Content negotiation (RDF, HTML)
 • Support for hybrid discovery
 • Integration of social features (tags, comments,
   ratings)
iServe Browser
Linked Open Data Cloud
iServe Context
iServe Architecture
SWEET
SWEET Workflow
SWEET: Initial State
Current status of the
annotation in the form   Input: HTML description of the Web API                  Addition of
of a tree structure      (local representation of the HTML, which is             the HTML tags
                         used as a basis for the annotation process)




                                 Dynamics, APIs and Services / Hands-on SWEET/iServe and WSMT - 44
Identifying Service Properties
Semantic Annotation
SWEET Architecture
OMNIVOKE
OmniVoke Context
OmniVoke Architecture
TYPICAL APPLICATION
SCENARIO
SAMPLE APPLICATIONS
Example 1: House Hunting …
… public transport …
… schools




            www.tauntonschool.co.uk
Behind the Scenes


Public Data and Services
                                      Service Broker
                         publishing
           Real estate
                                                       discovery

           Train stations

             Bus stops

              Schools                                  invocation
WATCH’N’BUY
Our Model

 gr:Offering            wnb:reference          wnb:Annotation     wnb:annotates           wnb:SpatioTemporalEntity


 gr:offers, gr:seeks, gr:saw        dc:creator     dc:created                       wnb:atTime    wnb:atPosition


                                                                        time:Temporal                    ir:Region
                       foaf:Agent                  xsd:dateTime             Entity

 wnb: http://watchnbuy.kmi.open.ac.uk/ontologies/annotation#
                                                                                  tl:onTimeline   ir:regionOf
 ma-ont: http://www.w3.org/ns/ma-ont#
 gr: http://purl.org/goodrelations/v1#
 foaf: http://xmlns.com/foaf/0.1/
 dc: http://purl.org/dc/elements/1.1/
                                                                                    ma-ont:MediaResource
 time: http://www.w3.org/2006/time#
 tl: http://purl.org/NET/c4dm/timeline.owl#
 ir: http://www.w3.org/2004/02/image-regions#
Architecture

  Watch 'n' Buy Linked Data Provider               Watch 'n' Buy Player                  Watch 'n' Buy Portal


         Watch 'n' Buy Core                  Product Metadata Importer                Video Metadata Importer

Annotation     User           Review     hProduct       Amazon              eBay       YouTube         TV Data
 Manager      Manager         Manager    Importer       Importer          Importer     Importer        Importer



                                                    Linked Services Infrastructure (iServe/OmniVoke)




               RDF
             Repository
                                        hProduct                   HTML



                              hProduct
CURRENT AND FUTURE WORK
ParkJam




                                       70



      http://parking.kmi.open.ac.uk/
Sharing Human Body Processes

         http://www.vph-share.eu/




                                    Personalised
          Patient Avatar




                                        Model
Cardiovascular Workflow
COMPOSE
The Internet of Services
    – Unified Service Description Language (USDL)


           Service Transformation stands for a value-driven, smooth and effective provision of services
            along the Global Service Delivery Supply Chain
           Service Transformation implies that Services are being
                      Described considering business, operational and legal requirements
                      Maintained, extended and assembled where needed
                      Applying a common notation named USDL




                       Service     Service    Service      Service       Service        Service
                       Provider    Hoster     Gateway     Aggregator     Broker      Channel Maker




                                                        See also: http://www.internet-of-services.de/index.php?id=24

© SAP 2010 / Page 73
USDL
http://www.internet-of-services.com/
Linked-USDL
http://www.linked-usdl.org/
Threats to Society
A New Era of Socio-Inspired
               Technology

  New socially interactive               Better understanding,
     ICT enabled by                            monitoring,
  complexity theory and                and management of global
   novel social science                society enabled by new ICT
                               influences


computer                     complexity                 social
  science                      science                  science
                               influences
Planetary
                   Nervous




                                          Participatory
                    System




                                            Platform
                                             Global
            high-level semantically
            meaningful information



                    Living
                    Earth
                  Simulator


complex predictions (e.g “financial crisis likely”)
Summary
• Linked data now a mainstream mechanism for sharing
  data on the Web
• Now a requirement for application development support
  – Especially within emerging Linked Data portals
• Linked services
  – Services which consume and produce linked data
  – Described as Linked Data
  – Approaches and principles
• Technologies supporting Linked Services
     • Models: MicroWSMO, WSMO-Lite and the Minimal Service Model
     • Tools: iServe, SWEET, OmniVoke….
• Validation of approach through diverse application
  scenarios
• Linked-USDL for services at the business level
Acknowledgements
• BBC slides adapted from Jem Rayfield
  http://www.slideshare.net/JemRayfield/mark-logic-
  usergroup2012
• Internet of Services adapted from SAP including Axel
  Fasse http://www.slideshare.net/drleidig/linked-usdl-at-
  the-fiware-architects-weeks-in-madrid
• Chris Bizer, Jacek Kopecky, Ning Li, Dong Liu, Maria
  Maleshkova, Carlos Pedrinaci
• Funded by the SOA4All, NoTube, PlanetData and VPH
  Share projects
Thanks
• More details at: iserve.kmi.open.ac.uk



• Interested in a PhD Studentship or internship?
  – John.domingue@open.ac.uk

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Linked services for the Web of Data

  • 1. Linked Services for the Web of Data John Domingue, Knowledge Media Institute, The Open University, UK STI International, Austria
  • 2. Overview • Linked data introduction – Linked data successes • Linked Services – Approaches and principles – Technologies supporting Linked Services • Models: MicroWSMO, WSMO-Lite and the Minimal Service Model • Tools: iServe, SWEET and OmniVoke • Sample applications – House hunting – Integrating advertising and video in Watch‟n‟Buy • Current and future work • Summary
  • 5. RDF = Subject, Property, Value Triples
  • 6. Triples combine to make Graphs
  • 7. Linked Data Principles Set of best practices for publishing structured data on the Web in accordance with the general architecture of the Web. 1. Use URIs as names for things. 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful RDF information. 4. Include RDF statements that link to other URIs so that they can discover related things. Tim Berners-Lee, http://www.w3.org/DesignIssues/LinkedData.html, 2006
  • 15. People, photos, friends and the Web
  • 16.
  • 19.
  • 20.
  • 22. Where does my money go?
  • 26. Linked Data and Services • Provide a platform for building applications on top of Linked Data • Connect services and semantic formats within the Web context • Ease the tasks associated with building applications from online service components
  • 27. Web APIs J. Mosser: “Open APIs: state of the market”, Glue conference 2011
  • 29.
  • 30.
  • 31. Linked Services Principles • Services described as Linked Data – Inputs, outputs, functionality, etc is described using RDF(S) and using existing vocabularies • Consume and produce RDF – Applications may contain „standard services‟ too • Process layer on top of the Web of Data
  • 35. LPML Analysis & SWEET & SOWER deployment Discovery Monitoring Invocation Process Editor incl. BPEL-based execution environment incl. TG, Optimizer, DTC Process modeling Service Process annotation SOA4All execution Process Lifecycle
  • 37. iServe Key Features • Support for several SWS formalisms – WSMO-Lite, MicroWSMO, SAWSDL, OWL-S • Supports access via – Web Application - iServe Browser – Read and Write RESTful API – Linked Data principles – SPARQL endpoint – Content negotiation (RDF, HTML) • Support for hybrid discovery • Integration of social features (tags, comments, ratings)
  • 42. SWEET
  • 44. SWEET: Initial State Current status of the annotation in the form Input: HTML description of the Web API Addition of of a tree structure (local representation of the HTML, which is the HTML tags used as a basis for the annotation process) Dynamics, APIs and Services / Hands-on SWEET/iServe and WSMT - 44
  • 52.
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 59. Example 1: House Hunting …
  • 61. … schools www.tauntonschool.co.uk
  • 62.
  • 63.
  • 64. Behind the Scenes Public Data and Services Service Broker publishing Real estate discovery Train stations Bus stops Schools invocation
  • 66.
  • 67. Our Model gr:Offering wnb:reference wnb:Annotation wnb:annotates wnb:SpatioTemporalEntity gr:offers, gr:seeks, gr:saw dc:creator dc:created wnb:atTime wnb:atPosition time:Temporal ir:Region foaf:Agent xsd:dateTime Entity wnb: http://watchnbuy.kmi.open.ac.uk/ontologies/annotation# tl:onTimeline ir:regionOf ma-ont: http://www.w3.org/ns/ma-ont# gr: http://purl.org/goodrelations/v1# foaf: http://xmlns.com/foaf/0.1/ dc: http://purl.org/dc/elements/1.1/ ma-ont:MediaResource time: http://www.w3.org/2006/time# tl: http://purl.org/NET/c4dm/timeline.owl# ir: http://www.w3.org/2004/02/image-regions#
  • 68. Architecture Watch 'n' Buy Linked Data Provider Watch 'n' Buy Player Watch 'n' Buy Portal Watch 'n' Buy Core Product Metadata Importer Video Metadata Importer Annotation User Review hProduct Amazon eBay YouTube TV Data Manager Manager Manager Importer Importer Importer Importer Importer Linked Services Infrastructure (iServe/OmniVoke) RDF Repository hProduct HTML hProduct
  • 70. ParkJam 70 http://parking.kmi.open.ac.uk/
  • 71. Sharing Human Body Processes http://www.vph-share.eu/ Personalised Patient Avatar Model Cardiovascular Workflow
  • 73. The Internet of Services – Unified Service Description Language (USDL)  Service Transformation stands for a value-driven, smooth and effective provision of services along the Global Service Delivery Supply Chain  Service Transformation implies that Services are being  Described considering business, operational and legal requirements  Maintained, extended and assembled where needed  Applying a common notation named USDL Service Service Service Service Service Service Provider Hoster Gateway Aggregator Broker Channel Maker See also: http://www.internet-of-services.de/index.php?id=24 © SAP 2010 / Page 73
  • 77. A New Era of Socio-Inspired Technology New socially interactive Better understanding, ICT enabled by monitoring, complexity theory and and management of global novel social science society enabled by new ICT influences computer complexity social science science science influences
  • 78. Planetary Nervous Participatory System Platform Global high-level semantically meaningful information Living Earth Simulator complex predictions (e.g “financial crisis likely”)
  • 79. Summary • Linked data now a mainstream mechanism for sharing data on the Web • Now a requirement for application development support – Especially within emerging Linked Data portals • Linked services – Services which consume and produce linked data – Described as Linked Data – Approaches and principles • Technologies supporting Linked Services • Models: MicroWSMO, WSMO-Lite and the Minimal Service Model • Tools: iServe, SWEET, OmniVoke…. • Validation of approach through diverse application scenarios • Linked-USDL for services at the business level
  • 80. Acknowledgements • BBC slides adapted from Jem Rayfield http://www.slideshare.net/JemRayfield/mark-logic- usergroup2012 • Internet of Services adapted from SAP including Axel Fasse http://www.slideshare.net/drleidig/linked-usdl-at- the-fiware-architects-weeks-in-madrid • Chris Bizer, Jacek Kopecky, Ning Li, Dong Liu, Maria Maleshkova, Carlos Pedrinaci • Funded by the SOA4All, NoTube, PlanetData and VPH Share projects
  • 81. Thanks • More details at: iserve.kmi.open.ac.uk • Interested in a PhD Studentship or internship? – John.domingue@open.ac.uk

Notas del editor

  1. 27 Live Video Steams for Olympics Live Stats overlaysStats -> Ontology driven aggregations
  2. Open Graph Protocol Inspired by Dublin Core, link-rel canonical, Microformats, and RDFa.3,000,000 likes per day! ‘Like’ buttons now appearing across all websites. These now generate With associated data
  3. 700 billion minutes per month on Facebook900 million content pages30 billion pieces of contentGenerating a graph of people, photos, friends and online resources
  4. Supports RDFa Lite a lightweight version of RDFa which can be used to embed RDF into web pages
  5. Google knowledge graphBased on Metaweb’s Freebase
  6. 8665 datasets
  7. 31billion statements, 450M links
  8. So how do we link to this wealth of data?We have our own repository of service descriptions within the cloud. We are the first and still only service repository in this space. The are created using a variety of tools. Note that when creating our descriptions we can rely on existing descriptions in the cloud. In the same way as one web page can point to another to expand a description.
  9. Which can produce data for this large semantic cloud
  10. Now I come to a first of 3 examples. This one from a recent project. Imagine that you are moving to a new area and you are looking for a house. You would be concerned with
  11. Depending on your circumstances
  12. Within one of my projects (soa4all) we have developed an iPhone App to support this. Its available in the store and called the soa4all real estate finder
  13. Mulberry school and others
  14. Services over public data (to the singers in the virtual choir)Service broker is like the conductor. Services are published in our broker. An engine translates between user actions and details of invoking services (each service may have its own idiosyncratic way of being invoked)User interacts with the iPhone Appdiscovery based on user preferences and location -> services are available Services are not fixed (like singers for each performance). adding more for crime statistics also based on public data.
  15. elasticity of heart muscles, another modelling blood flow, another for different dysfucntionsPatient avatar: a digital personalised representation of a patient for diagnosis and treatmentIn the media domainWe have a new project which started in Spring which will look at sharing processes related to the human body across Europe to support research and patient care. One of the processes to be modelled will be the human heart. The idea is that across varies labs in Europe there will be a bits and pieces of data and software – e.g. Our broker will be used to put these pieces together into a coherent whole and also to integrate into patient specific data leading to personalised patient avatars – a digital represention of your relevant processes supporting diagnosis and treatment.