Semantic%20Web:%20Applications

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Semantic Web Applications

• Vagan Terziyan

Industrial Ontologies Group
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Content

• Semantic Annotation
• Semantic Communication
• Semantic Search
• Semantic Integration
• Semantic Personalization
• Semantic Proactivity
• Semantic Games

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Semantic Web Applications Business Categories

• Knowledge Management
• Enterprise Application Integration
• eCommerce

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Risc vs. Impact Tradeoff
Impact
eCommerce
Enterprise Application Integration
Knowledge Management
Risc
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Technology Roadmap for Applications
Semantic Communication
2
Semantic Search
Semantic Games
3
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Semantic Annotation
1
Semantic Integration
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Semantic Proactivity
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Semantic Personalization
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P2P
Web Services
Agent Technology
Machine Learning
Semantic Web (SW)
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1. Semantic annotation
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Ontology-based User Interface

• Simple user data ontology for mobile phones

Auto-generated form for data
Data to store in every instance of defined
information model

• Model of users data and other resources
• Contacts (phone numbers, names etc.)
• Notes (some pieces of text)
• Calendar (with some events assigned)

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BANK Data annotation
In order to make miscellaneous data gathered and
used later for some processing, every piece of
data needs label assigned, which will denote its
semantics in terms of some ontology. Software
that is developed with support of that ontology
can recognize the data and process it correctly
in respect to its semantics.
Annotated data (RDF)
Ontology of gathered data
Web forms and dialogs generated
Processing of data by some other semantic-aware
applications
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2. Semantic Communication
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Semantic Communication (human-to-human)
request for semantic call
user
Search agent, provides semantic match
functionality
Semantic annotation
Shared ontology
users
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Semantic Communication (human-to-machine)
request for semantic call
Condition Monitoring Expert
Search agent, provides semantic match
functionality
Semantic annotation
Shared ontology
Field devices
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Semantic Communication (machine-to-human)
request for semantic call
Smart device
Search agent, provides semantic match
functionality
Semantic annotation
Shared ontology
Fault diagnostics experts
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Semantic Communication (machine-to-machine)
request for semantic call
Smart device
Search agent, provides semantic match
functionality
Semantic annotation
Shared ontology
Field devices
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Semantic Call

• Examples
• Connect me with someone who can sell me cheep (lt
  500) rowing boat in Jyväskylä
• Connect me with a blond girl (21-25) who wants
  to meet a guy (26) tonight to go to dancing club
  in Jyväskylä, etc.

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Architecture for a Mobile P-Commerce Service
Terziyan V., Architecture for Mobile P-Commerce
Multilevel Profiling Framework, IJCAI-2001
International Workshop on "E-Business and the
Intelligent Web", Seattle, USA, 5 August 2001, 12
pp.
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P-Commerce Service providers
Automatic
Mapping and Transactions
via resources and users annotations
Service User
Service User
Service User
Service User
Service User
Service User
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Machine-to-Machine Semantic Communication
P2P ontology
P2P ontology
Heterogeneous machines can understand each
other while exchanging data due to shared
ontologies
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Agent-to-Agent communication
Peer-to-Peer
- - - - - - - - - - -
Semantic annotation of the local data enables its
intelligent processing by software. Ontologies
provide interoperability between heterogeneous
peers.
Phone calls are also possible between mobile
terminal agents. They are performed without human
participation in order to exchange local
information.
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Agent-to-Agent communication
semantics enables
intelligent data processing
Business
ontological relations
define possible
Cooking
cooperation between
domain agents
Health
shared ontology
ensures
interoperability
Whatever
?
annotate problem domains
into related ontologies
programm software
basing on the ontologies
semantically enrich
data basing on ontologies
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3. Semantic Search
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Semantic Web Semantic Search
request for semantic search
user
Search agent, provides semantic match
functionality
Semantic annotation
Shared ontology
Web resources / services / DBs / etc.
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Semantic Search

• What to search?

Where to search?
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Semantic Facilitators for Web Information
Retrieval (2004)
InBCT Tekes PROJECT Chapter 3.1.3 Industrial
Ontologies and Semantic Web (year 2004)

• Generic Semantic Search Facilitator concept,
  architecture and ideas for future utilization of
  semantic wrappers for non-semantic search systems
• Implementation of Semantic Search Assistant for
  Google with semantic interface and domain
  ontology.

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Semantic Web and Information Retrieval

• SW promises many advantages and benefits, but
• We are in transition to the Semantic Web
• Resources are not yet annotated semantically
• No semantics available for resource retrieving
• Semantic-enabled search of non-semantic data ???
• Yes, we need a Semantic Facilitator

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Semantic Facilitator

• What is it?
• Search service that uses other services
• Uses many search types (WWW, DB, directory, file
  system)
• Knows and learns how to search for X better
• Can integrate results from DB with search on the
  Web(find name in database and persons photo
  from web page)
• Can filter returned results
• Intelligent tool that really understands what
  users want to get, because it accepts semantic
  query
• What it is not?
• Search engine, indexing tool, registry, etc.
• Data storage, database browser, etc.

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How does it work?

1. Get request
2. Translate request into series of queries to the
   used search engines, databases, data storages
   Taking into account the semantics of searched
   data
3. Combine returned results, filter non-relevant (if
   keyword search was used) results
4. Return set of best-try results

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Basic idea
SemanticSearchFacilitator is Semantic Interface
for search mechanism, which use combination and
integration of the set of other existing search
engines. This interface provides additional
semantic enhancement and filtering of a complex
information retrieval.
Local search
? search
DB search
Web search
SemanticSearchFacilitator
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Ontology Personalization
Ontology Personalization is mechanism, which
allows users to have own conceptual view and be
able to use it for semantic querying of search
facilities.
Search
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Enabling the Semantic Search
Semantic Search Enhancement
Common (linguistic) ontology
Semantic Search Assistant (Facilitator) uses
ontologically (WordNet) defined knowledge about
words and embedded support of advanced
Google-search query features in order to
construct more efficient queries from formal
textual description of searched information.
Semantic Search Assistant hides from users the
complexity of query language of concrete search
engine and performs routine actions that most of
users do in order to achieve better performance
and get more relevant results.
Domain ontology
(
)
Query
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4. Semantic Integration
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Semantic Web Semantic Integration
Integrated resource
Semantic annotation
Shared ontology
Web resources / services / DBs / etc.
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Semantic Web-Supported Sharing and Integration of
Web Services
Different companies would be able to share and
use cooperatively their Web resources and
services due to standardized descriptions of
their resources.
P2P ontology
P2P ontology
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Corporate/Business Hub
Hub ontology and shared domain ontologies
Partners / Businesses
Companies would be able to create Corporate
Hubs, which would be an excellent cooperative
business environment for their applications.
What parties can do
What parties achieve
Publish own resource descriptions
Software and data reuse
Advertise own services
Automated access to enterprise (or partners)
resources
Lookup for resources with semantic search
Seamless integration of services
Ontologies will help to glue such
Enterprise-wide / Cooperative Semantic Web of
shared resources
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Ontology-Based Mobile Services Integration
(Composition, Orchestration)
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5. Semantic Personalization
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Mobile Location-Based Service in Semantic Web
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Contextual and Predictive Attributes
Predictive attributes
Contextual attributes
Mobile customer description
Ordered service
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Simple distance between Two Preferences with
Heterogeneous Attributes (Example)
Wine Preference 1 I prefer white wine served at
15 C
where
Wine Preference 2 I prefer red wine served at
25 C
Importance Wine color ?1 0.7 Wine
temperature ?2 0.3
d (white, red) 1
d (15, 25) 10/((30)-(10)) 0.5
D (Wine_preference_1, Wine_preference_2) v
(0.7 1 0.3 0.5) 0.922
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Prediction of Customers Actions
here I had massage
here I had nice wine
here I washed my car
d1
d2
d3
here I had great pizza
d4
here I made hair
d5
I am here now. There are my recent
preferences 1. I need to wash my car
0.1 2. I want to drink some wine 0.2 3. I
need a massage 0.2 4. I want to
eat pizza 0.8 5. I need to make
my hair 0.6 Make a guess what I will
order now and where !
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Agents Spatial Belief Example
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Agents Spatial Desire Example
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Agents Spatial Intentions Examples
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Agents Spatial Action Example
Jonker C., Terziyan V., Treur J., Temporal and
Spatial Analysis to Personalize an Agents
Dynamic Belief, Desire and Intention Profiles,
In M. Klush et al. (eds.), Cooperative
Information Agents VII Proceedings of the 7-th
International Workshop on Cooperative Information
Agents (CIA-2003), Helsinki, Finland, August
27-29, 2003, Lecture Notes in Artificial
Intelligence, V. 2782, Springer-Verlag, pp.
289-315.
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6. Semantic Proactivity
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Agents in Semantic Web
3. Wait a bit, I will give you some pills
1. I feel bad, pressure more than 200, headache,
Who can advise what to do ?
Agents in Semantic Web supposed to understand
each other because they will share common
standard, platform, ontology and language
4. Never had such experience. No idea what to do
2. I think you should stop drink beer for a
while
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The Challenge Global Understanding eNvironment
(GUN)
How to make entities from our physical world to
understand each other when necessary ?..
Its elementary ! But not easy !! Just to make
agents from them !!!
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GUN Concept
2. I have some pills for you
1. I feel bad, temperature 40, pain in stomach,
Who can advise what to do ?
Entities will interoperate through OntoShells,
which are supplements of these entities up to
Semantic Web enabled agents
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Semantic Web Before GUN
Semantic Web Applications
Semantic Web applications understand, (re)use,
share, integrate, etc. Semantic Web resources
Semantic Web Resources
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GUN Concept All GUN resources understand each
other
Real World objects
Real World Object OntoAdapter OntoShell
GUN Resource
GUN
OntoShells
Real World objects of new generation
(OntoAdapter inside)
OntoAdapters
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Web Services for Smart Devices
Smart industrial devices can be also Web Service
users. Their embedded agents are able to
monitor the state of appropriate device, to
communicate and exchange data with another
agents. There is a good reason to launch special
Web Services for such smart industrial devices to
provide necessary online condition monitoring,
diagnostics, maintenance support, etc.
"OntoServ.Net"
OntoServ.Net Semantic Web Enabled Network of
Maintenance Services for Smart Devices,
Industrial Ontologies Group, March 2003,
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Global Network of Maintenance Services
"OntoServ.Net"
OntoServ.Net Semantic Web Enabled Network of
Maintenance Services for Smart Devices,
Industrial Ontologies Group, March 2003,
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Embedded Maintenance Platforms
Embedded Platform
Based on the online diagnostics, a service agent,
selected for the specific emergency situation,
moves to the embedded platform to help the host
agent to manage it and to carry out the
predictive maintenance activities
Host Agent
Maintenance Service
Service Agents
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Semantic Web in Networked BusinessEnvironment
Networked Business Environment requires new
advanced ways of data and knowledge
management Industrial Maintenance domain is a
good application case for the concept of the
Networked Business Environment Networked
Maintenance Environment will bring all benefits
of the knowledge management, delivering
value-added services and integration of businesses
In a networked business environment Metso will
be a business hub controlling the flow of
information in the network of installed Metso
devices and solutions, and Metsos customers and
partners.
Semantic Web technology provides standards for
metadata and ontology development such as
semantic annotations (Resource Description
Framework) and knowledge representation (Web
Ontology Language). It facilitates
interoperability of heterogeneous components,
authoring reusable data and intelligent,
automated processing of data. Semantic Web is
an enabling technology for the future Networked
Business Environment
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S m a r t R e s o u r c e

• SmartResource concept is combining the emerging
  Semantic Web, Web Services, Peer-to-Peer and
  Agent technologies for the development of a
  global and smart maintenance management
  environment, to provide Web-based support for the
  predictive maintenance of industrial devices by
  utilizing heterogeneous and interoperable Web
  resources, services and human experts

Industrial Ontologies Group, January 2004
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Industrial Resources

• Classes of resources in maintenance systems
• Devices - increasingly complex machines,
  equipment, etc., that require costs-demanding
  support
• Processing Units (Services) embedded, local and
  remote systems, for automated intelligent
  monitoring, diagnostics and control over devices
• Humans (Experts) qualified users of the system,
  operators, maintenance experts, a limited
  resource that should be reused

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Smart Maintenance Environment
Experts
Devices with on-line data
exchange
data
Maintenance
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SmartResource (Stage 1)
Define Semantic Web-based framework for
unification of maintenance data and
interoperability in maintenance system

• Research and Development
• Resource State/Condition Description Framework
  (RSCDF) based on Semantic Web and extension of
  RDF (Resource Description Framework)
• RSCDF adapters (wrappers)
• for devices, services and experts
• - browsable devices
• - application-expert interface
• - RSCDF-enabled services

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SmartResource (Stage 2)
Development of agent-based resource management
framework and enabling meaningful resource
interaction

• Adding agents to resources
• Making resource proactive
• Enabling communication
• with resource
• Implementation of agent-communication scenarios
• service learning
• remote diagnostics

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SmartResource (Stage 3)
Development of networked maintenance environment

• Development of P2P agent-communication system
• Resource Discovery
• Maintenance Data Knowledge Integration
• Certification and credibility assessment of
  services
• Research of the Resource Goal/Behavior
  Description Framework
• Semantic modelling of a resource proactive
  behavior
• Exchanging integrating models of resource
  (maintenance) behavior
• Testing on-the-field using
• Real devices
• Existing diagnostic software as Web-services
• Experts

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Maintenance Networking Environment
Semantic Web environment
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P2P networking
- network of hubs
- highly scalable
- fault-tolerable

• supports dynamic changes
• of network structure

• does not need
• administration

• Why to interact?
• Resource summarizes opinions from multiple
  services
• Services learns from multiple teachers
• One service for multiple similar clients
• Resources exchange lists of services
• Services exchange lists of clients.

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Integrating services
Device
Labelled data
Service
Service
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Integrating knowledge
Service

• Service builds classification model many
  techniques are possible, e.g.
• own model for each device
• one model from several devices of the same
  type (provides device experience exchange) .

Diagnostic model
1
Diagnostic model
n
Device
Device
Device
Labelled data
Device
Device
Device
Labelled data
Labelled data
Labelled data
Labelled data
Labelled data
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Certification
Sure, there are security threats as in any
open environment. Security is to be ensured using
existing solutions for Internet environment.
Existence of certification authorities is
required in the network. Certificates gained by
services and trust to the certificate issuer are
factors that influence optimal service selection.
The quality of service is evaluated by users as
well.
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SmartResource Expectations
Adaptation of resources (devices, services,
experts) to the Environment
Support for service learning
Resource Proactivity
Resource Agent
Interaction One device many services
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Smart Maintenance Environment
exchange
data
Maintenance
On-line learning
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Customers of the system
Experts, which want to provide their experience
and knowledge in domain of remote diagnostics,
condition monitoring and maintenance of
industrial devices.
Experts
Web Service and Application providers
Producers of the Field Devices
Producers of the Field Devices, which are
interested to provide more effective product
maintenance and remote diagnostic for their
customers.
Providers of the diagnostic, maintenance,
condition monitoring services, which want to make
them available for customers through the net.
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Smart Maintenance Environment
IT Providers applications, services, solutions
Manufacturer of industrial products
User
Expert
Service- mediator
Sensor
Web Service
Smart-Device
Smart-System
Application
Smart Maintenance Environment
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Application domainsTelemedicineWellness
Doctor/Expert
Doctor/Expert
Service
Health Maintenance
Service
Service
Health Maintenance
Symptom data
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Telemedicine
Health Center
On a beach
At university
Anywhere

• built-in sensors (blood pressure, heart
  beat-rate, temperature)
• support for on-body medical sensors or sensors in
  a clothes

Fishing
Health Maintenance without barriers Anytime and
Anywhere
In the office
Outside
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Application domainsBuilding Conditioning
Automation of the Building Conditioning via
a House Maintenance System, embedded sensor
system of a house state, local house- condition
alarm system, and global system for house remote
diagnostics and predictive maintenance
Service
Service
Service
Expert
Expert
Expert
House systems sensors
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7. Semantic-enabled Games
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Related European Research Initiatives (1)

• There are several going on EU funded
  projects, which are targeting various aspects of
  emerging Semantic Web. Among most strong
  consortiums and initiatives are
• OntoWeb1 network with more than 100 academic
  and industrial participants, which creates a
  technical roadmap of the next generation Web and
  provides guidelines to industrial and commercial
  applications
• SWAP2 (Semantic Web and Peer-to-Peer)
• provides a comprehensive study of the potential
  of Semantic Web and Peer-to-Peer for knowledge
  management and plan to provide an appropriate
  integrated software environment
• SWWS3 (Semantic Web Enabled Web Services)
• researching for scalable mediation between
  different and heterogeneous services based on
  semantic-driven descriptions and business logic
• 1 http//www.ontoweb.org
• 2 http//swap.semanticweb.org
• 3 http//swws.semanticweb.org

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Related European Research Initiatives (2)

• SEWASIE4 (Semantic Web and Agents in Integrated
  Economies)
• fights the problem of access to heterogeneous
  data sources on the Web
• SCULPTEUR5 (Semantic and Content-Based
  Multimedia Exploitation for European Benefit)
• develops the technology to create, manipulate
  and manage cultural archives to make European
  cultural heritage accessible to all
• MOSES6 (Modular and Scalable Environment for
  the Semantic Web)
• sets out to create scalable ontology based
  Knowledge Management System and ontology-based
  search engine that will accept queries and
  produce answers in natural language
• and many other projects
• 4 http//www.sewasie.org
• 5 http//www.sculpteurweb.org
• 6 http//www.hum.ku.dk/moses/