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Linked services: Connecting services to the Web of Data

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Keynote from the International Conference on e-Business Engineering, September 2013. The talk covers a short integration to Linked Data, our approach to building applications on top of the Web of Data (which we term Linked Services) and a number of applications in the areas of house hunting: crowdsourcing car parking, sharing human body processes. The talk also covers recent work on transforming SAP's Unified Service Description Language to a Linked Data format.

Keynote from the International Conference on e-Business Engineering, September 2013. The talk covers a short integration to Linked Data, our approach to building applications on top of the Web of Data (which we term Linked Services) and a number of applications in the areas of house hunting: crowdsourcing car parking, sharing human body processes. The talk also covers recent work on transforming SAP's Unified Service Description Language to a Linked Data format.

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Linked services: Connecting services to the Web of Data

  1. 1. Linked Services: Connecting Services to the Web of Data John Domingue with Carlos Pedrinaci Knowledge Media Institute, The Open University
  2. 2. Overview • Linked Data introduction – Linked Data successes – Linked Data applications • Linked Services – Approaches and principles – Preliminary example – Supporting tools and vocabularies • Sample applications – Sharing Human Body processes – Crowdsourcing car parking – Integrating advertising and video in Watch’n’Buy • Service Marketplaces with Linked USDL • Summary
  3. 3. LINKED DATA INTRODUCTION
  4. 4. Semantic Web Stack
  5. 5. RDF = Subject, Property, Value Triples
  6. 6. Triples combine to make Graphs
  7. 7. Linked Data Principles 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 Courtesy of Chris Bizer Set of best practices for publishing structured data on the Web in accordance with the general architecture of the Web.
  8. 8. LINKED DATA SUCCESSES
  9. 9. I ‘Like’ Casablanca
  10. 10. People, photos, friends and the Web
  11. 11. LINKED DATA APPLICATIONS
  12. 12. (291)
  13. 13. Where does my money go?
  14. 14. ASBOrometer
  15. 15. Taken from http://ldif.wbsg.de/
  16. 16. LINKED SERVICES APPROACH AND PRINCIPLES
  17. 17. 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
  18. 18. A PRELIMINARY EXAMPLE
  19. 19. Behind the Scenes Train stations Bus stops Schools Real estate Public Data and Services publishing Service Broker Invocation Engine discovery invocation
  20. 20. SUPPORTING TOOLS AND VOCABULARIES
  21. 21. SWEET & SOWER LPML deployment Process Editor Discovery incl. Optimizer Process Lifecycle Service annotation Process modeling Process execution Analysis & Monitoring incl. BPEL-based execution environment SPICES
  22. 22. LINKED SERVICE VOCABULARIES
  23. 23. WSDL
  24. 24. SAWSDL
  25. 25. WSMO-Lite Terms Ontology rdf:type rdfs:Class rdfs:subClassOf owl:Ontology ClassificationRoot rdfs:subClassOf rdfs:Class NonFunctionalParameter rdf:type rdfs:Class Condition rdf:type rdfs:Class Effect rdf:type rdfs:Class
  26. 26. RESTFUL SERVICES/WEB APIS
  27. 27. • Microformat –Collaboration with Amit Sheth • Introduces the service model structure –Service –Operations • Address, method –Inputs, Outputs (only their existence) hRESTS
  28. 28. MicroWSMO • Extends hRESTS –mref for model references –lifting, lowering • Applies WSMO-Lite semantics
  29. 29. MicroWSMO & WSMO-Lite
  30. 30. Minimal Service Model, WSMO-Lite
  31. 31. Authentication
  32. 32. SUPPORTING TOOLS
  33. 33. ISERVE: A SEMANTIC SERVICE REPOSITORY
  34. 34. 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)
  35. 35. iServe Browser
  36. 36. Linked Open Data Cloud
  37. 37. iServe Architecture 46
  38. 38. iServe Service Discovery • Several Mechanisms –Simple SPARQL-based –Inputs/Outputs logic-based using RDFS reasoning –Functional Classifications with RDFS reasoning –Similarity analysis based on iMatcher
  39. 39. iServe Discovery RESTful API •/data/disco/func-rdfs?class=C1 &class=C2 &... –uses RDFS functional classification annotations and returns those services that are related to all the functional categories Ci (which are URIs). •/data/disco/io-rdfs?f={and|or}&i=C1I &i=C2I &o=C1O &... –uses ontology annotations of inputs and outputs and returns services for which the client has suitable input data (CiI) and/or (depending on the parameter f for function) which provide the outputs requested by the client (CiO). •/data/disco/imatch?name=L –returns all services ranked according to the Levenshtein (other mechanisms available) string similarity of the service label with the string L.
  40. 40. iServe Atom-based Discovery • Discovery returns an Atom feed with the results and provides Atom feed combinators - Union, Intersection, Subtract •http://iserve.kmi.open.ac.uk/data/atom/union?f= /data/disco/func- rdfs?class=http://iserve.kmi.open.ac.uk/2010/05/s3eval/func.rdfs%2523ProximitySearch &f=/data/disco/io- rdfs?o=http://iserve.kmi.open.ac.uk/2010/05/s3eval/data.rdfs%2523ATMLocation
  41. 41. Integrated with a Recommender • Distributed solution • Linked User Feedback • RS4All
  42. 42. SWEET: SEMANTIC WEB API ANNOTATION TOOL
  43. 43. SWEET Workflow
  44. 44. SWEET Demo (hRESTS)
  45. 45. SWEET Demo (Ontologies)
  46. 46. SWEET Architecture
  47. 47. SAMPLE APPLICATIONS
  48. 48. Sharing Human Body Processes PatientAvatar Personalised Model Cardiovascular Workflow
  49. 49. ParkJam 60 http://parking.kmi.open.ac.uk/
  50. 50. Architecture RDF Repository Watch 'n' Buy Core Annotation Manager User Manager Review Manager Watch 'n' Buy Linked Data Provider Watch 'n' Buy Player Watch 'n' Buy Portal Product Metadata Importer hProduct Importer Amazon Importer eBay Importer Video Metadata Importer YouTube Importer TV Data Importer hProduct HTML hProduct Linked Services Infrastructure (iServe/OmniVoke)
  51. 51. Data Modelling • Videos (Media) and their Fragments – W3C Media Ontology: http://www.w3.org/TR/mediaont-10/ – W3C Image Regions: http://www.w3.org/2004/02/image-regions (404ed) – Watson cache of W3C Image Regions: http://kmi- web05.open.ac.uk:81/cache/d/6d9/d02c/1e2ba/66d39 a0e85/063395385293e283d – W3C Media Fragment URIs: http://www.w3.org/TR/media-frags/ • Annotation – Open Annotation Collaboration: http://www.openannotation.org/spec/
  52. 52. Our Model wnb:Annotation wnb:SpatioTemporalEntitywnb:annotates ir:Region gr:Offering wnb:reference wnb:atPosition ma-ont:MediaResource time:Temporal Entity foaf:Agent wnb:atTime tl:onTimeline ir:regionOf xsd:dateTime dc:createddc:creatorgr:offers, gr:seeks, gr:saw wnb: http://watchnbuy.kmi.open.ac.uk/ontologies/annotation# 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/ time: http://www.w3.org/2006/time# tl: http://purl.org/NET/c4dm/timeline.owl# ir: http://www.w3.org/2004/02/image-regions#
  53. 53. SERVICE MARKETPLACES
  54. 54. The Future Internet – Enabler for Global Business Networks Manu- facturing Urban Management eEnergyTransport Logistic …. Network of the Future Cloud Computing Internet of Things Internet of Services Internet of the Future Consumers Suppliers Wholesalers Retailers Carriers Manufacture r Governments © SAP 2010 /
  55. 55. The Internet of Services – Global Service Delivery Supply Chain A Single Market for Services SaaS, On-Demand Enterprise Suites Cloud Services Service Marketplaces Multi-Enterprise BPP B2B Gateways Business Process Outsourcing Business Process Platform Service Delivery Framework Service Aggregator Service Hoster Service Provider Service Gateway Service Broker Service Channel Maker Service-Oriented Architecture © SAP 2010 / Page 67
  56. 56. © SAP 2010 / Page 68 Service Aggregator Service Hoster Service Provider Service Gateway Service Broker Service Channel Maker The Internet of Services – Unified Service Description Language (USDL) See also: http://www.internet-of-services.de/index.php?id=24  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
  57. 57. TRESOR
  58. 58. Summary (1/2) • Linked Data –Based on 4 simple principles –Take-up now by major Web and Media players –Linked Data portals now emerging –Lack of support for applications • Linked Services –Approach to creating applications on top of Linked Data –Built upon • Vocabularies: Minimal Service Model, MicroWSMO, WSMO-Lite • Tools: iServe, SWEET …
  59. 59. Summary (2/2) • Linked service applications include: –SOA4RE for house hunting –ParkJam supporting crowd-sourced car park availability –Towards patient avatar’s • Linked USDL for Service Marketplaces
  60. 60. Credits and URIs • iServe - http://iserve.kmi.open.ac.uk/ • Linked USDL - http://www.linked-usdl.org/ • SOA4All funded under FP7 - http://www.soa4all.eu/ • VPH-Share funded under FP7 - http://www.vph- share.eu/ • ParkJam - http://parking.kmi.open.ac.uk/ • Also based on work of: –Jacek Kopecky, Dong Liu, Maria Maleshkova

Hinweis der Redaktion

  • Thanks for the introduction. Thanks for coming. Flavour of the work related to the notion of a Future Internet.
  • 3,000,000 likes per day! ‘Like’ buttons now appearing across all websites. These now generate With associated data
  • 700 billion minutes per month on Facebook900 million content pages30 billion pieces of contentGenerating a graph of people, photos, friends and online resources
  • Supports RDFa Lite a lightweight version of RDFa which can be used to embed RDF into web pages
  • Google knowledge graphBased on Metaweb’s Freebase
  • 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.
  • Which can produce data for this large semantic cloud
  • 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
  • Mulberry school and others
  • 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.
  • The AuthenticationMechanism class has six subclasses, corresponding to com-mon authentication mechanisms, Credentials class has a number of instances includingAPI Key , Username , Password and OAuth Credentials , which can be combinedto produce composite credentials, such as authentication through username andPassword.The TransmissionMedium has two instances(ViaHTTPHeader and ViaURI ), used to describe that the credentials are sentby using only the URI or through constructing an HTTP header.he composedOf relationship as well as the class AuthenticationMech-anism , which can have further subclasses, represent points of extensibility forthe ontology. The Service class has a relationship to the ServiceAuthentication class, which has three instances including All , Some and None that are used topoint out that the service requires authentication for all its operations, for onlysome of them or for none of them.
  • 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.
  • SAP 4th largest software producer in the world by revenue Microsoft, IBM and Oracle. 109,000 customers 120 countriesSAP with 12.46B euro of revenue Very much interested in the Internet of the Future
  • Complete Ecosystem for value added services based on Service Objects Lower barriers to develop, select, combine and use value added servicesObject DescriptionCapture data exposed and its semanticsCapture objects capabilities (frequency of data provisioned, processing capabilities)Capture contextual information e.g., geolocationBetter support Discovery, Composition and UseReusable assetsSemantic Sensor Networks Ontology (W3C)Domain ontologiesDynamic Large Scale Data Processing InfrastructureCapture data streams and their provenanceSense making of large quantities of streaming data (e.g., feature inferencing, data correlation, etc)Secure end-to-end channelsReusable AssetsProvenance vocabulary (W3C)Domain ontologiesAdvance Data Mining and Machine Learning algorithmsObjects Virtualisation as ServicesExpose Objects as reusable servicesVirtualisation of sensors into services for the development of advanced applications through compositionUse of capabilities for optimised decomposition and deploymentReusable assetsMinimal Service ModelAI planning and (de)composition algorithms

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