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.
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
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.
24. 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
28. Behind the Scenes
Train stations
Bus stops
Schools
Real estate
Public Data and Services
publishing
Service Broker
Invocation
Engine
discovery
invocation
36. • Microformat
–Collaboration with Amit Sheth
• Introduces the service model structure
–Service
–Operations
• Address, method
–Inputs, Outputs (only their existence)
hRESTS
43. 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)
47. 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
48. 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.
49. 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
50. Integrated with a Recommender
• Distributed solution
• Linked User Feedback
• RS4All
72. 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 …
73. 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
74. 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