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Program
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Endorsed By
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Sunday, August 22nd
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15.30
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15.45
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Welcome / Introduction
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15.45
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16.15
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Quantitative
and Qualitative Evaluation of the OntoLearn Ontology Learning System – Presentation
Roberto Navigli, Paola Velardi, Alessandro
Cucchiarelli, Francesca Neri:
Universita La Sapienza
Roma & Universita Politecnica
delle Marche
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16.15
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16.45
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A Task-based
Approach for Ontology
Evaluation
Robert Porzel,
Rainer Malaka - European Media Laboratory,
Germany – Presentation
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16.45
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17.30
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Invited Talk: Ontology Learning and Populating in Genomics
Claire Nedellec:
INRA, France
Two main Machine
Learning approaches are applied to ontology building from corpus:
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conceptual clustering
for learning the generality relation (is-a) from cooccurrences. No annotation is needed beforehand. However, the scope of the learning target is difficult to control. The validation of the result is done by hand and time-consuming.
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learning patterns from labeled examples for extracting instances of given ontological
relations. The annotation
of the learning examples is time consuming. However, the quality and the scope of the learning results are predictable and the validation is done by computer.
We will illustrate the complementarities, the advantages and limitations of these two approaches on the popular problem of ontology learning in functional genomics on a subtle bacterium, Bacillus subtilis.
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17.30
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18.00
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Tea Break
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18.00
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18.30
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Measuring the Specificity of Terms for Automatic Hierarchy
Construction – Presentation
Pum-Mo Ryu, Key-Sun
Choi: KAIST, Korea
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18.30
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19.00
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Discovering Knowledge in Texts for the Learning of DOGMA-Inspired Ontologies
– Presentation
Marie-Laure Reinberger, Peter Spyns: University of Antwerpen & Vrije Universiteit Brussel
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19.00
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19.30
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Learning Taxonomic Relations from Heterogeneous Evidence
– Presentation
Philipp Cimiano, Aleksander Pivk,
Lars Schmidt-Thieme, Steffen Staab: University
of Karlsruhe & Jozef Stefan Institute,
Ljubljana & University of Freiburg
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Monday, August 23rd
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Topic and Motivation
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Ontologies are formal, explicit specifications of shared conceptualizations, representing
concepts and their
relations that are
relevant for a given domain of discourse. Currently, ontologies are mostly developed (including ontology construction, extension, mapping and merging) as well as used (ontology population through knowledge markup) by a manual process, which is very
ineffective and may
cause major barriers to
their large-scale use in such areas as Knowledge Discovery and Semantic
Web. The expected central role of ontologies in the organization and functioning
of the Semantic Web has
been well documented in
recent years. Somewhat less traditional is the role
of ontologies in incremental
approaches to Knowledge
Discovery, in which ontologies
and machine learning methods are used in combination to mine, interpret and (re-)organize knowledge.
As human language is a primary mode of knowledge transfer, linguistic analysis of
relevant documents for ontology learning and population seems a viable option. More precisely, automation of these tasks can be
implemented by a combined use of linguistic analysis and machine learning approaches for text mining. The workshop will therefore be concerned with reports on the development of such methods, but specifically also with the quantitative evaluation
of these methods.
Automatic methods for text-based ontology learning and population have developed over recent years (e.g. results from the ECAI-2000,
IJCAI-2001, ECAI-2002
workshops on Ontology Learning and the KCAP-2001, ECAI-2002,
KCAP-2003 workshops on Knowledge Markup / Ontology
Population), but a remaining
challenge is to evaluate in a quantitative manner
how useful or accurate the extracted ontology classes, properties and instances are. In fact, this is a central
issue as it is currently very hard to compare methods and approaches, due to the lack of a shared understanding of the task at hand. The core theme of the workshop therefore will be to develop such a shared understanding through the definition of a clear task (and corresponding sub-tasks), identify resources needed for the task/sub-tasks
and to discuss how best
to develop an open source evaluation platform.
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Areas of Interest
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Submissions are invited on these topics in Ontology Learning and
Population (OLP):
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* Evaluation Methodologies
and Metrics for OLP
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Including Experience and Best Practice
from Related
Evaluation Efforts in the
Context of CLEF, TREC, SENSEVAL, etc.
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* Datasets
and Resources for the Evaluation of OLP
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* Definition of Sub-Tasks
for OLP
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Extraction of Taxonomy, Class-hierarchy
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Extraction of Class-properties, Relations
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Extraction of Class-instances, Individuals
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* Definition of Related
Tasks
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Ontology Extension,
Evolution
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Ontology Mapping
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Ontology Merging
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* Text-based
Approaches for OLP, for instance (Combinations of):
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NLP and Linguistic
Analysis for OLP
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(NLP-based) Text-mining for OLP
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(Ontology-aware)
Information Extraction for
OLP
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* OLP in the
Context of the Semantic Web
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* OLP in the
Context of Knowledge
Discovery
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Organizing Committee
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Program Committee
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AIFB
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Siegfried Handschuh, Steffen Staab,
York Sure
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Bar Ilan University
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Ido Dagan
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DFKI
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Paul Buitelaar, Andreas
Eisele, Michael Sintek
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IRIT, Toulouse
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Nathalie Aussenac-Gilles
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IRST
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Bernardo Magnini
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Josef Stefan Inst.
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Marko Grobelnik
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KDLabs
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Jörg-Uwe Kietz
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LOA-CNR
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Aldo Gangemi
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MIG-INRA
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Claire Nedellec
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NCSR “Demokritos”
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Georgios Paliouras
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Partners HealthCare
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Vipul Kashyap
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Univ. Antwerpen
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Walter Daelemans
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Univ. Basque Country
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Eneko Agirre
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Univ. Paris 13, LIPN
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Adeline Nazarenko
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Univ. Poly. Madrid
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Asuncion Gomez-Perez
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Univ. Roma “La Sapienza”
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Paola Velardi
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Univ. Roma “Tor Vergata”
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Roberto Basili
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Univ. Saarland
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Thierry Declerck
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Univ. Sheffield
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Fabio Ciravegna, Hamish Cunningham, Yorick Wilks
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USC/ISI
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Eduard Hovy
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XRCE
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Eric Gaussier
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Workshop Attendance and Registration
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All workshop participants must register for ECAI-2004
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