OLP3 – 3rd Workshop on Ontology Learning and Population

ECAI 2008 - July 22nd, 2008 - Patras, Greece

Supported By             

Endorsed By          ACL-SIG on Computational Semantics


Invited Speaker     Enrique Alfonseca, Google Europe




Introduction to OLP and overview of the workshop


Session 1: Ontology Population


OPTIMA: An Ontology Population System

Sang-Soo Kim, Jeong-Woo Son, Seong-Bae Park, Se-Young Park, Changki Lee, Ji-HyunWang, Myung-Gil Jang and Hyung-Geun Park


Coffee Break


Session 2: Ontology Learning


Presentation - An Interactive Pattern-Based Approach for Extracting Non-Taxonomic Relations from Texts

Nathalie Aussenac-Gilles, Marie Chagnoux and Nathalie Hernandez


Presentation - Supervised Relation Extraction for Ontology Learning from Text Based on a Cognitively Plausible Model of Relations

Massimo Poesio, Eduard Barbu, Claudio Giuliano and Lorenza Romano


Presentation - A Distributional Approach to Evaluating Ontology Learning Methods Using a Gold Standard

Elias Zavitsanos, Georgios Paliouras and George Vouros




Session 3: Ontology LearningLinguistic and Web2.0 Approaches


Presentation - Learning Domain-Specific Framenets from Texts

Roberto Basili, Cristina Giannone and Diego De Cao


Presentation - Semantic Grounding of Tag Relatedness in Social Bookmarking Systems

Ciro Cattuto, Dominik Benz, Andreas Hotho and Gerd Stumme


Coffee Break


Invited Talk: Large-scale Learning of Semantic Relationships from Text Documents

Enrique Alfonseca (Google)                                                                                        



Workshop Description

Ontologies are increasingly used in knowledge management and discovery, Semantic Web, NLP and other intelligent applications. Ontologies formalize the intensional aspects of a domain, whereas the extensional part is provided by a knowledge base that contains assertions about instances of concepts and relations as defined by the ontology. The process of defining and instantiating a knowledge base according to an ontology is referred to as knowledge markup or ontology population, whereas semi-automatic support in ontology development is usually referred to as ontology learning.

Ontology learning is one of the most challenging fields in artificial intelligence. AI research has contributed valuable methods for ontology learning and population from documents, including intelligent document analysis and text understanding, methods for the extraction of rules, relations and axioms, tools for reasoning and for resolving conflicts between ontologies. At the same time there has been an increasing amount of work devoted to the evaluation of such methods, with an emphasis primarily on the evaluation of ontology-based information extraction for ontology population and taxonomy extraction for ontology learning. Finally, some initial work has been done also in the integration of automated methods for ontology learning and population in the broader framework of knowledge discovery, engineering and management.

In the context of this workshop we want to foster the further development of such work towards the development of advanced methods and tools that

·        use the network and knowledge structure of web 2.0 data and of existing ontologies

·        include the extraction of higher-level knowledge such as rules and axioms

·        can be evaluated in the context of independently defined tasks

·        are more deeply integrated with knowledge engineering methodologies

·        use advanced methods such as abduction

Topics of interest

Knowledge sources for ontology learning and population


Use of web resources such as Web directories, e.g. in concept naming


Exploitation of web 2.0 data and social networks in ontology learning and population


Knowledge reuse in ontology learning, e.g. by bootstrapping from existing ontologies found on the web (ontology libraries and search)

Knowledge extraction for ontology learning and population


Identification of concept formation on the basis of textual data, including the extraction of concept intension (natural language definition) and extension (instances)


Extraction of higher-level ontological knowledge such as rules and axioms from textual or other data

Evaluation of ontology learning and population


More complete benchmarks and evaluation measures for ontology learning that go beyond lexical overlap and taxonomy evaluation


Task-based evaluation of ontology learning, e.g. in the context of question answering, machine translation, textual entailment

Methods in ontology learning and population


Similarity-based and dimensionality reduction methods for concept and relation discovery


The role of abduction, induction and deduction in ontology learning, and multi-strategy approaches


Handling of inconsistency and uncertainty in ontology population and learning

Ontology evolution


Integration of ontology learning with knowledge engineering methodologies and the role of the user-interface and visualization


Personalizing ontologies with knowledge discovery


Ontology evolution methodologies

Important Dates

April 13th

Submission Deadline

May 13th


June 9th 

Camera-ready Version

July 22nd



Submission will be electronic (http://www.easychair.org/conferences/?conf=olp3) and should follow the ECAI submission style (http://www.ece.upatras.gr/ecai2008/substyles.htm) but contrary to ECAI, submission for OLP3 will not be blind!


Papers must be submitted as PDF (no other formats will be accepted), no later than April 7th, 2008 (12 pm GMT) and should not exceed five (5) pages - including references.


Please note that papers may be submitted to ECAI (main conference) as well as to OLP3 with the right to retract your submission from OLP3 if accepted at ECAI.

A special issue of an AI journal comprising extended versions of the best papers will be pursued, if the quality of the papers is judged sufficient by the program committee.

Organizing Committee

Paul Buitelaar

DFKI, Germany

Philipp Cimiano

AIFB, Univ. of Karlsruhe, Germany

George Paliouras

NCSR “Demokritos”, Greece

Myra Spiliopoulou

Otto-von-Guericke University Magdeburg, Germany

Program Committee

Khurshid Ahmad

Trinity College Dublin, Ireland

Enrique Alfonseca

Google Europe, Switzerland

Nathalie Aussenac-Gilles

IRIT- CNRS Toulouse, France

Christopher Brewster

University of Sheffield, UK

Silvana Castano

Università degli Studi di Milano, Italy

Massimiliano Ciaramita

Yahoo! Research, Spain

Nigel Collier

National Institute of Informatics, Japan

Ludger van Elst

DFKI, Germany

Alfio Ferrara

Università degli Studi di Milano, Italy

Gregory Grefenstette

CEA, France

Peter Haase

AIFB, Universität Karlsruhe, Germany

Siegfried Handschuh

DERI Galway, Ireland

Andreas Hotho

University of Kassel, Germany

Eduard Hovy       

USC, Information Sciences Institute, USA

Diana Maynard

University of Sheffield, UK

Roberto Navigli

University of Rome La Sapienza, Italy

Adeline Nazarenko

LIPN - Université Paris-Nord, France

Patrick Pantel

USC, Information Sciences Institute, USA

Massimo Poesio

University of Trento, Italy

Ralf Moeller

Hamburg University of Technology, Germany

Mehrnoush Shamsfard

Shahid Beheshti University, Iran

Elena Simperl

Universität Innsbruck, Austria

Rion Snow

Stanford University, USA

Steffen Staab

University of Koblenz-Landau, Germany

Vojtech Svatek

University of Economics, Prague, Czech Rep.

George Vouros

University of the Aegean, Greece

Paola Velardi

Università di Roma "La Sapienza", Italy

Johanna Völker

AIFB, Universität Karlsruhe, Germany

Dominic Widdows

Google, USA

Takahira Yamaguchi

Keio University, Japan

Workshop Registration

All workshop participants must register for ECAI 2008.


Please note also that at least one author must be registered by the early registration date to present the paper at the workshop. Proof of registration fees payment should be provided together with the camera-ready version of the paper.

Previous OLP Workshops

Workshop on Ontology Learning and Population (OLP) at ECAI 2004

2nd Workshop on Ontology Learning and Population (OLP2) at ACL/COLING 2006

Related Publications

Paul Buitelaar, Philipp Cimiano (Eds.) Ontology Learning and Population: Bridging the Gap between Text and Knowledge Frontiers in Artificial Intelligence and Applications Series, Vol. 167, IOS Press, February 2008

Paul Buitelaar, Philipp Cimiano, Bernardo Magnini (eds.) Ontology Learning from Text: Methods, Evaluation and Applications Frontiers in Artificial Intelligence and Applications Series, Vol. 123, IOS Press, July 2005