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Semantic Sky: Cloud services integration using semantic web technologies. Agenda. Introduction: Semantic web technologies basics Semitic sky architecture Examples Conclusion. Introduction. Cloud computing.

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semantic sky cloud services integration using semantic web technologies

Semantic Sky:Cloud services integration using semantic web technologies

Prof. DimitarTrajanov

08 Jun 2011, CERTH, Greece

agenda
Agenda
  • Introduction:
  • Semantic web technologies basics
  • Semitic sky architecture
  • Examples
  • Conclusion
cloud computing
Cloud computing
  • Cloud computing refers to the on-demand provision of computational resources (data, software) via a computer network
  • Cloud Computing Stack
    • SaaS - Software as a Service
    • PaaS - Platform as a Service
    • IaaS - Infrastructure as a Service
the information we work with in our every day live
The information we work with in our every day live
  • Obtained from different sources:
    • Web (Facebook, Twitter, …)
    • Intranet (e-mail, Enterprise applications, …)
    • Local data (local documents, …)
  • The number of information sources is increasing rapidly
    • Increased number of publicly available services
    • Increasing number of cloud services with specialized functionalities
    • Increased number of enterprise application
  • Depending on information type, we mainly take some actions, e.g. we share them or add them into a ToDo list
the problem
The problem
  • Interchange data among information sources
  • Need of complex and composite actions
  • Actions require a certain amount of time (get/copy the data, change the context, transfer the data, execute an action in destination service)
  • Services and the data are placed on different locations and infrastructures
motivation
Motivation
  • To develop a software platform which will provide the users with a unified and simple composite approach to the different services they use, and with a simple flow of information from one infrastructure to another.
  • To come to such a design, a large number of partial problems will have to be solved
    • Mechanisms for detection of the entities which are found within texts and information that we get from different services.
    • Based on the context in which the user is working, to offer actions (services) that can be performed on the entities.
    • Integration with local working environment of the user.
solution semantic sky
Solution: Semantic Sky
  • The system is called “SemanticSky”, because it is an environment where many cloud services will exist and interact with each-other
  • It is based on semantic web technologies
  • Reuse of known ontologies (FOAF, AIISO, University Ontology,GeoNames, …)
related work
Related work
  • There are projects that are focused on the connectivity of different cloud infrastructures (mOSAIC, SITIO, …)
  • Microsoft Outlook plug-in
    • Xobni ffers fast search and people-based navigation of email archives.
    • Mashin organizes information extracted from email history contextually.
  • Google mail plug-in
related work1
Related work
  • Babylon-Enterprise is a web-configured client-server system based on a Windows program (Babylon- Enterprise Client) installed on the end-user’s workstation and an enterprise application server (Babylon-Enterprise Server).
    • Gives the ability to access all enterprise information and data from every working environment.
  • Greplin is a personal search engine that allows you to search all your online data in one place.
a layered approach
A Semantic Web PrimerA Layered Approach
  • The development of the Semantic Web proceeds in steps
    • Each step building a layer on top of another

Principles:

  • Downward compatibility
  • Upward partial understanding
semantic web open standards
Semantic Web Open Standards
  • RDF – Store data as “triples”
  • OWL – Define systems of concepts called “ontologies”
  • Sparql – Query data in RDF
  • SWRL – Define rules
  • GRDDL – Transform data to RDF
rdf triples

Predicate

Subject

Object

RDF “Triples”
  • the subject, which is an RDF URI reference or a blank node
  • the predicate, which is an RDF URI reference
  • the object, which is an RDF URI reference, a literal or a blank node

Source: http://www.w3.org/TR/rdf-concepts/#section-triples

rdbms vs triplestore
RDBMS vs Triplestore

Person Table

S

P

O

Subject Predicate Object

001 isA Person

001 firstName Jim

001 lastName Wissner

001 hasColleague 002

002 isA Person

002 firstName Nova

002 lastName Spivack

002 hasColleague 003

003 isA Person

003 firstName Chris

003 lastName Jones

003 hasColleague 004

004 isA Person

004 firstName Lew

004 lastName Tucker

f_name

jim

nova

chris

lew

ID

001

002

003

004

l_name

wissner

spivack

jones

tucker

Colleagues Table

SRC-ID

001

001

001

001

002

002

002

002

003

003

003

003

004

004

004

004

TGT-ID

001

002

003

004

001

002

003

004

001

002

003

004

001

002

003

004

ontologies
Ontologies
  • The term ontology originates from philosophy
    • The study of the nature of existence
  • Different meaning from computer science
    • An ontology is an explicit and formal specification of a conceptualization
  • Ontologies provide a shared understanding of a domain (semantic interoperability)
    • overcome differences in terminology
    • mappings between ontologies
  • There are many available onotologies for different domains
typical components of ontologies
A Semantic Web PrimerTypical Components of Ontologies
  • Terms denote important concepts (classes of objects) of the domain
    • e.g. professors, staff, students, courses, departments
  • Relationships between these terms: typically class hierarchies
    • a class C to be a subclass of another class C' if every object in C is also included in C'
    • e.g. all professors are staff members
further components of ontologies
Further Components of Ontologies
  • Properties:
    • e.g. X teaches Y
  • Value restrictions
    • e.g. only faculty members can teach courses
  • Disjointness statements
    • e.g. faculty and general staff are disjoint
  • Logical relationships between objects
    • e.g. every department must include at least 10 faculty
system overview
System Overview

Sparql Endpoint

Semantic Sky Resource Retrieval

Ontology

Desktop-client

Cloud Service 1

Semantic Sky

Cloud Service 2

Action Invocation

Cloud plug-in

Cloud Service 3

Browser Plug-in

knowledge base
Knowledge base
  • RDF data store
  • Apache Lucene is used as indexing engine
  • Each triple (statement), rdf:class and rdf:property are indexed as separated entities (Lucene Document)
  • Extensible
knowledge base extension
Knowledge base extension
  • Owl/Rdf file upload
    • Using Jena API to extract semantic resources
    • Calls the indexer to index resources
  • Owl/Rdf URI
    • Paste the link to the Owl or Rdf document
    • Jena extract the resources and passes them to the Lucena indexer
  • SPARQL endpoints
    • By providing a URL to the endpoint
    • Connect to the endpoint address and fetch all data
    • Lucene indexes the fetched data from the endpoint
web service repository
Web service repository
  • Used for faster service discovery
  • Semantically annotated web services
    • Service input types
    • Service output types
  • Any ontology can be used for annotation of the web services
  • Extensible
extending the ws repository
Extending the WS Repository
  • Using existing web services
    • Annotating using the SAWSDL standard
    • Annotation tool developed
    • Import the annotated WSDL file into the repository
  • Creating new web services
    • Develop the web service
    • Repeat the steps for existing web services
  • Using REST web services
    • Tool for semantic mapping of REST services (in progress)
extensibility in action
Extensibility in action
  • We have system that enables Task Management and exports web services for this.
  • We want to add new functionality about Task Management.
  • What do we do to enable this?
    • Import the ontology for this domain, if there is no any
    • Annotate the services (Preferably with our tool)
    • Define actions
    • It is on and can be used 
data linking and inference engine
Data Linking and Inference Engine
  • System entry point
    • Accepts text
    • Return semantic resources correlated with the text
  • For each token (word) in the text, we extract all resources related to it
  • Extraction is made using Apache Lucene Search
  • All Lucene entities retrieved from the search are converted to semantic resources
data linking and inference engine1
Data Linking and Inference Engine

Ontology

index

Data Linking and Inference Engine

Find the resources

for the text

from the index

Ontology

For each resource,

get its properties

SPARQL

endpoints

Group resources by type

Type : [

{p1:v1,p2:v2,..,uri:#res1},

{p1:v1',p3:v3',..,uri:#res2}

]

action search
Action Search

Action Search

Semantic

WS

Repository

Type : [

{p1:v1,p2:v2,..,uri:#res1},

{p1:v1',p3:v3',..,uri:#res2}

]

Align resource types as inputs

Find all operations from the

Repository for these inputs

Are

there entries in the

repository

Semantically

annotated

web services

no

yes

Find all compositions

with these inputs and store

them in the repository

Assemble action

XML result

<Action>

<id>uid</id>

<inputs>....</inputs>

</Action>

operations retrieval
Operations Retrieval
  • Searching operations (web service methods) from the repository
  • Service compositions are made when possible
  • Uses the types of the extracted resources to find the operations
  • User_Defined_Input
    • rdfs:Class used to denote that this input will be rendered as input text at the client side
    • Implicit input type
      • it will be placed in the inputs list, even when no resource from this type is extracted
    • The user must provide the value for this type
action form
Action Form

User_Defined_Input

ui generator
UI Generator

<Action>

<id>uid</id>

<inputs>....</inputs>

</Action>

Type : [

{p1:v1,p2:v2,..,uri:#res1},

{p1:v1',p3:v3',..,uri:#res2}

]

UI Generator

Find transformer for

Resource type

Transformers

Transform the resource

Transform the actions

action invocation
Action Invocation
  • The generated form contains all parameters for action invocation
  • Single service for action invocation
    • It assembles the parameters and invokes the actual services
    • The result is returned back to the user
implementation details data integration
Implementation detailsData integration

- Enterprise data

- Opening the data

example university data
Example:University data
  • Most of today information systems (IS) store their data in relational databases
  • This data is published in a structured way, in RDF format on the Semantic Web
  • What we publish? basic information about the Faculties and deeper information about our CSE Faculty (Institutes, Modules, Programs, Courses, Subjects, Employees)
  • Few universities, most of them in the UK, have already started open data projects, which are still in development
semantic data publishing
Semantic data publishing
  • There are many tools for publishing the content of relational databases on the Semantic Web like D2R Server, Oracle Spatial 11g, Asio Semantic Bridge, SquirrelRDF and many others
  • We use the D2R Server
  • D2R Server enables RDF and HTML browsers to navigate the content of the database,and allows applications to query the database using the SPARQL query language
  • http://www4.wiwiss.fu-berlin.de/bizer/d2r-server/
open linked data
Open Linked data
  • Our goal is five star data – data linked to other people’s data to provide context
  • We connect to well known ontologies, which already have definitions for our types of data
  • D2RQ Mapping Language is a declarative mapping language for describing the relation between an ontology and an relational data model
ontologies1
Ontologies
  • For describing our data we need few well known ontologies
  • The Web Ontology Language (OWL)
  • FOAF - ontology describing persons, their activities and their relations to other people and objects, it is used for the employees
  • The Academic Institution Internal Structure Ontology (AIISO) - provides classes and properties to describe the internal organizational structure of an academic institution
  • University Ontology – same purpose as AIISO, but contains some additional features needed for describing our data
  • GeoNames Ontology - makes it possible to add geospatial semantic information to the Word Wide Web
sparql endpoint
Sparql Endpoint
  • Changes in the mapping .n3 file for connecting with the ontologies have to be made manually
  • After the .n3 file is edited, it can be run with D2R Server and in the Sparql Endpoint, queries can be written using the prefixes from the ontologies
  • The Sparql Endpoint shows the data in triples: subject, predicate and object
  • http://e-tech2.feit.ukim.edu.mk/open-data/snorql/
university open data example
University Open Data Example
  • This query shows basic information about the Professor Trajanov and the courses he teaches
  • http://e-tech2.feit.ukim.edu.mk/open-data/snorql/?describe=http%3A%2F%2Fe-tech2.feit.ukim.edu.mk%2Fopen-data%2Fresource%2Fdbo.EMPLOYEES%2F64
university open data example1
University Open Data Example
  • This query shows basic information about the subject Network Programming and the courses of that subject
  • http://e-tech2.feit.ukim.edu.mk/open-data/snorql/?describe=http%3A%2F%2Fe-tech2.feit.ukim.edu.mk%2Fopen-data%2Fresource%2Fdbo.SUBJECTS%2F1
d2r server mapping tool
D2R Server Mapping tool
  • Manually editing the .n3 file is time consuming, so we created application called the D2R Server Mapping Tool to connect to the ontologies
  • The user first enters the database which wants to be published, then the application generates .n3 file using the D2R server
  • The Mapping tool then converts the .n3 file to .rdf file, format which can be easily shown in a visual xml-alike tree
  • The user can choose some class or property from the tree and just add or remove reference from an ontology
  • Ontologies can also be added and removed from the application
implementation details data access
Implementation details Data access

Cloud plug-in

Desktop application

google gadgets
Google Gadgets

Embed application's UI into Gmail, Calendar, Spreadsheets and Sites, using the OpenSocial standard

what are gmail gadgets
What are Gmail Gadgets?
  • Custom HTML & JavaScript components
  • Run within an iframe
  • Extend Gmail with additional functionality
  • Implement the Google gadgets API
  • Two types of Gmail Gadgets
    • Sidebar Gadgets
    • Contextual Gadgets
gmail contextual gadget
Gmail Contextual Gadget
  • Displayed at the bottom of individual email messages
  • Triggered by contextual clues
    • E-mail subject
    • E-mail sender
    • E-mail body
  • Example: the YouTube contextual gadget
    • Triggered whenever a YouTube link appears in the e-mail body.
gmail contextual gadget implementation
Gmail Contextual Gadget Implementation
  • Extractor
    • Detects contextual clues
    • Determines which types of content will trigger the gadget
    • Passes the triggering content to the gadget
  • Gadget Specification
    • Takes action based on the content passed in from an extractor
    • Client-side logic and UI
semantic sky contextual gadget
Semantic Sky Contextual Gadget
  • Uses an email body extractor to extract data
    • Triggered on every e-mail message *
    • Extracts the e-mail body text
  • Sends the extracted text to Semantic Sky server via the services provided by the core module
  • Receives and parses the JSON response
  • Generates contextual action forms based on the response received
  • Renders the UI HTML in the gadget iframe

*Except on e-mails containing non ASCII letters. This is a known Google Gadget API bug

generating action forms
Generating Action Forms
  • Generated dynamically by the client script, depending on the response received from the server
  • A list of available actions for the identified entities, along with the types of their input parameters are received in the response JSON object.
  • The client script generates input fields for each input parameter. The input fields are either select fields, or plain text input fields depending on the type of the input parameter the field is generated for.
  • Select fields are pre-populated with entities identified and returned from the server that match the input type of the input parameter
action form1
Action Form

User_Defined_Input

invoking actions
Invoking Actions
  • Input values are extracted from each input field in the action form
  • Values are packed into a JSON object
  • Request is sent to the service responsible for receiving action invocation requests on the server
  • The server parses the received object, and invokes the requested action
desktop context extracor
Desktop context extracor
  • Internet based infrastructure for collaboration with desktop application
  • Desktop side interconnection with the services
    • The communication with the public services is established with public API’s
    • Semantic annotated web services connection with the Semnatic sky cloud
    • The OpenCalais web service used for automatic semantic annotation
  • Selected text search
technology used and os interaction
Technology used and OS interaction
  • C# .NET (framework v4.0) platform is used for developing the application
  • Win32 API for interaction with the OS
    • System hook intercepts windows messages and detects mouse and keyboard activity while the application runs in background.
  • Hotkey activation of the application
    • Initiates copying of the selected text in the Clipboard
    • Gets whole text from the currently active window for the semantic search
    • Activates the application
  • Clipboard data retrieval (text or image)
    • When the application is activated, data from the Clipboard is retrieved automatically.
    • Processing of the data begins
architecture
Architecture
  • The application is based on 4 main objects
    • Sources
    • Objects
    • Types
    • Actions
  • Other parts of the system
    • External ontology
    • Web services
sources
Sources
  • Information from the external services based on the selected text search
  • Classes which make the connection to the external web services on the cloud
    • Facebook API
    • Gmail API
    • Wikipedia public SOAP web service
    • Open Calais public SOAP web service
    • Semantic sky RESTfull web services
objects and types
Objects and types
  • The information retrieved from the services is parsed into objects
  • Every object has a type
  • Object information depends on the type
  • Object type is determined by semantic search on external ontology
actions
Actions
  • Based on the type, different actions can be performed on the objects
    • Simple actions
      • Write email
      • Write on facebook wall etc
    • Semantic sky web services
      • Gets all actions for the annotated objects
use case of the application
Use case of the application
  • Select text on any window
  • Hotkey click
    • gets the selected text in the application for processing
  • Found object are shown by type and by source
  • Actions for the found objects on the right side of the application
  • Execute actions
use of the gmail and facebook api
Use of the Gmail and Facebook API
  • Semantic desktop application uses API libraries to connect to Gmail and Facebook
  • Login is needed for both services to retrieve contacts
  • Simple actions with contacts
    • Write on the wall
    • Write email
use of the wikipedia and open calais
Use of the Wikipedia and Open Calais
  • Connection to Wikipedia information is established by SOAP web service
    • Retrieves related terms to the input search string
  • Open Calais service uses the CalaisDotNet library API
    • Uses semantic search to recognize objects, their type and relevance
use of the semantic sky source
Use of the Semantic Sky source
  • The desktop application is connected to the Semantic Sky source with 3 web services
    • textAnnotations – gets information about the semantic resources found in the text input
    • actionsForText – gets all actions for the semantic resources
    • invokeAction – invokes the selected action for a specific semantic resource
conclusion1
Conclusion
  • Semantic Sky is the framework which enables connectivity and integration, not only of different cloud services, but also of local data placed on the user machine.
  • Automation of the use of different services
  • Intelligent engine that proposes actions that could (or should) be executed by the user
  • Google contextual gadget developed
  • Desktop application developed (includes additional cloud integration)
  • We join the Open Data trend by publishing some of the faculty data
future work
Future work
  • Extending the core system with some public cloud services
  • Develop browser plug-in
  • Add personalization
  • Add system learning by example
  • Creating semantic copy/paste for entity transfer between applications (copy a person form Facebook and paste it in your CRM)
slide73

THANK YOU !!!

http://www.finki.ukim.mk/

dimitar.trajanov@finki.ukim.mk