 Copyright 2007 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Report on DERI, Galway Jan Zemanek (DIKE, UEP; SmILE, DERI)

Digital Enterprise Research Institute DERI, Galway in a nutshell & 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) Director: Prof. Dr. Stefan Decker  Vice director: Prof. Dr. Manfred Hauswirth around 100 members  2 stable Czech members: Dr. Tomas Vitvar, Vit Novacek – Tomas has launched his own weblog lately, you can find it at

Digital Enterprise Research Institute SmILE subcluster & 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)  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

Digital Enterprise Research Institute DINO ontology lifecycle scenario and framework 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

Digital Enterprise Research Institute Ontology development 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  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

Digital Enterprise Research Institute DINO ontology integration based on  Dynamic Integration of Medical Ontologies in Large Scale, Novacek, V.; Laera, L.; Handschuh, S.; article much more details in  D2.3.8v1 Report and Prototype of Dynamics in the Ontology Lifecycle

Digital Enterprise Research Institute DINO ontology integration scheme of the integration process phases of the integration  providing a master ontology  providing an extending ontology  alignment/negotiation  reasoning/management  ontology diff  triple sorter  mapping triples to natural language suggestions

Digital Enterprise Research Institute DINO integration scheme

Digital Enterprise Research Institute DINO phases of integration providing a master ontology providing an extending ontology  ontology learning – machine learning and NLP methods are used for a processing of relevant resources and extracting knowledge from them – is realised using Text2Onto  any “external” ontology can be provided – we can integrate e.g. different ontologies from the same domain or specialised/general ontologies

Digital Enterprise Research Institute DINO phases of integration alignment/negotiation  provided ontologies need to be reconciled since they cover the same domain, but might be structured differently  contsists of mappings between the concepts, properties, and relationships in provided ontologies  uses ontology alignment API developed by INRIA Rhone- Alpes

Digital Enterprise Research Institute DINO phases of integration reasoning/management  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  handles inconsistencies like sub-class hierarchy cycles, disjointness-subsumption, disjointness-instantiation  resulting ontology is passed to an ontology diff  uses Jena 2 Ontology API

Digital Enterprise Research Institute DINO phases of integration ontology diff  possible ontology extensions are equal to the additions that the merged ontology brings into the master ontology  the addition triples form a base to eventual ontology extension suggestions

Digital Enterprise Research Institute DINO phases of integration triple sorter  applies an ordering taking a relevance measure of possible suggestions into account (based on preferred and unwanted terms)

Digital Enterprise Research Institute DINO phases of integration mapping triples to natural language suggestions  produced suggestions are in a form of very simple natural language statements which are obtained directly from the sorted triples

Digital Enterprise Research Institute DINO integration manager original plans  DINO should have been a part of MarcOnt portal initially – MarcOnt portal ( –an environment for collaborative ontology development being developed at DERI, Galway  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 reality  DINO as a stand-alone Java application

Digital Enterprise Research Institute DINO integration manager

Digital Enterprise Research Institute DINO integration manager Demo

Digital Enterprise Research Institute Semantic web for Java developers interesting Java tools handling Semantic web technologies I encountered or had to deal with directly  SemVersion  RDF2Go  RDFReactor

Digital Enterprise Research Institute 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  version RDF models  commit and merge RDF models Semantic Version Manager  an implementation of SemVersion as a Protege plugin

Digital Enterprise Research Institute Semantic Version Manager

Digital Enterprise Research Institute Semantic Version Manager

Digital Enterprise Research Institute 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  Jena 2.4  Sesame 2.0 beta 6 (the latest release) used in  SemVersion  Aperture

Digital Enterprise Research Institute RDF2Go RDF2Go example code:

Digital Enterprise Research Institute 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  RDFReactor Java proxies are stateless Java proxies are generated automatically from RDF Schema

Digital Enterprise Research Institute RDFReactor example code:

Digital Enterprise Research Institute The very last slide Any (other) questions? Thank you for your attention!