Transcript Digital Enterprise Research Intitute

Digital Enterprise Research Institute
Report on internship @ DERI,
Galway
Jan Zemanek (DIKE, UEP; SmILE, DERI)
[email protected]
 Copyright 2007 Digital Enterprise Research Institute. All rights reserved.
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DERI, Galway in a nutshell
Digital Enterprise Research Institute
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http://www.deri.ie & http://blog.deri.ie/
DERI – Digital Enterprise Research Intitute at National
University of Ireland
„Making Semantic Web real.“
Research areas
Semantic Web (cluster)
 Semantic Web Services (cluster)
 eLearning (cluster)
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Director: Prof. Dr. Stefan Decker
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Vice director: Prof. Dr. Manfred Hauswirth
around 100 members
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2 stable Czech members: Dr. Tomas Vitvar, Vit Novacek
– Tomas has launched his own weblog lately, you can find it at
http://www.vitvar.com/blog/
www.deri.ie
SmILE subcluster
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http://smile.deri.ie/ & http://smile.deri.ie/blog/
SmILE stands for Semantic Information Systems and
Language Engineering Group
Group leader: Dr. Siegfried Handschuh
„focused around the application of Semantic Web and
Language Engineering techniques to support knowledge
acquisition and re-use in different settings“
leading project: NEPOMUK (Networked Environment for
Personal Ontology-based Management of Unified Knowledge)
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NEPOMUK aims to build a Social Semantic Desktop which will
present information in a well defined manner, which will be
processible by computer, and which will connect and exchange
data with other desktops
DINO ontology lifecycle scenario
and framework
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DINO stands for „Dynamics, INtegration and
Ontology“ or „Data and INtensive Ontology“
is a scenario and framework for practical handling
of dynamic and large data-sets in an ontology
lifecycle, focusing particularly on dynamic
integration of learned knowledge into
collaboratively developed ontologies
Ontology development
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ontologies are very likely subject to change given
the dynamic nature of domain knowledge
ontology construction is usually the result of
collaboration
it is not always feasible to process all the relevant
data and extract the knowledge from them
manually
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this implies a need for (partial) automation of ontology
extraction and management processes in dynamic and
data-intensive environments
– this can only be achieved by ontology learning
DINO ontology integration
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based on
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Dynamic Integration of Medical Ontologies in Large
Scale, Novacek, V.; Laera, L.; Handschuh, S.; article
much more details in
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D2.3.8v1 Report and Prototype of Dynamics in the
Ontology Lifecycle
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DINO ontology integration
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scheme of the integration process
phases of the integration
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providing a master ontology
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providing an extending ontology
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alignment/negotiation
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reasoning/management
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ontology diff
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triple sorter
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mapping triples to natural language suggestions
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DINO integration scheme
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DINO phases of integration
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providing a master ontology
providing an extending ontology
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ontology learning
– machine learning and NLP methods are used for a processing
of relevant resources and extracting knowledge from them
– is realised using Text2Onto
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any “external” ontology can be provided
– we can integrate e.g. different ontologies from the same
domain or specialised/general ontologies
DINO phases of integration
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alignment/negotiation
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provided ontologies need to be reconciled since they cover
the same domain, but might be structured differently
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contsists of mappings between the concepts, properties,
and relationships in provided ontologies
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uses ontology alignment API developed by INRIA RhoneAlpes
DINO phases of integration
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reasoning/management
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used for merging of the provided ontologies according to
statements in an „alignment ontology“
– the „alignment ontology“ consists of axioms merging classes,
individuals and properties
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handles inconsistencies like sub-class hierarchy cycles,
disjointness-subsumption, disjointness-instantiation
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resulting ontology is passed to an ontology diff
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uses Jena 2 Ontology API
DINO phases of integration
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ontology diff
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possible ontology extensions are equal to the additions
that the merged ontology brings into the master ontology
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the addition triples form a base to eventual ontology
extension suggestions
DINO phases of integration
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triple sorter
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applies an ordering taking a relevance measure of possible
suggestions into account (based on preferred and
unwanted terms)
DINO phases of integration
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mapping triples to natural language suggestions
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produced suggestions are in a form of very simple natural
language statements which are obtained directly from the
sorted triples
DINO integration manager
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original plans
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DINO should have been a part of MarcOnt portal initially
– MarcOnt portal (http://www.marcont.org/)
– an environment for collaborative ontology development being
developed at DERI, Galway
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DINO as a bunch of cooperating Protege(-OWL) plugins
– Semantic Version Manager plugin
– Protege plugin built upon SemVersion
– Collaborative Protege
– problems with 3rd party libraries used in Text2Onto and
GATE
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reality
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DINO as a stand-alone Java application
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DINO integration manager
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DINO integration manager
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Demo
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Semantic web for Java developers
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interesting Java tools handling Semantic web
technologies I encountered or had to deal with
directly
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SemVersion
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RDF2Go
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RDFReactor
www.deri.ie
SemVersion
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http://wiki.ontoworld.org/wiki/SemVersion
 developed mainly by Max Voelkel
 a versioning system for RDF and RDF ontologies
 backed by Sesame 2 (since v1.0.0 alpha)
 enables to
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version RDF models
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commit and merge RDF models
Semantic Version Manager
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an implementation of SemVersion as a Protege plugin
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Semantic Version Manager
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Semantic Version Manager
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RDF2Go
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http://wiki.ontoworld.org/wiki/RDF2Go
an abstraction over triple (and quad) stores
allows a programmer to code against RDF2Go
interface and thus to stay independent of the
underlying RDF store
supported RDF stores
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Jena 2.4
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Sesame 2.0 beta 6 (the latest release)
used in
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SemVersion
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Aperture
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RDF2Go
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RDF2Go example code:
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RDFReactor
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http://wiki.ontoworld.org/wiki/RDFReactor
a view of RDF data through object-oriented Java
proxies making using RDF natural for Java
developers
„Think in objects, not statements.“
all state information is in a RDF model in a RDF
store at all times
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RDFReactor Java proxies are stateless
Java proxies are generated automatically from RDF
Schema
RDFReactor
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example code:
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The very last slide
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Any (other) questions?
Thank you for your attention!
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