06-2008 Y. Jaques Yves Jaques ICIS Requirements Gathering, June 2008, Rome NeOn Lifecycle Support for Networked Ontologies

06-2008Y. Jaques

Yves JaquesICIS Requirements Gathering, June 2008, Rome

NeOn Lifecycle Support for Networked Ontologies

06-2008 FAO-FIESY. Jaques

What are ontologies? networks of concepts and relationships

taxonomies (parent/child) are just one type of relationship that can be modeled

includesorder

includesfamily

includesspecies


How are ontologies written? expressed using a modelling language such as

XML-based OWL:

using editors such as NeOn NTK, Protégé, Top Braid Semantic Works and Swoop.


Some OWL terminology Classes

a.k.a. concepts Object Properties

a.k.a. relations Individuals

a.k.a. instances Datatype properties

a.k.a attributes

Centrolophusniger

SPECIESFAMILYincludes species

Rudderfish

has

name

THING

CEO

has FAO alpha code


Ontologies are very expressive relationships can be constrained in many ways:


How are ontologies useful? Languages like OWL create a standard syntax

for describing hierarchies, taxonomies and classifications.

Many existing applications can edit, interpret and display OWL models.

Dissemination and sharing of models becomes simpler and more standardized.

Standard web services enable ontology discovery and distribution.

Local ontologies can be mapped to standard ontologies (we can agree to disagree)


How can they be shared? New platforms such as NeOn allow for the

distributed maintenance of ontologies, and cross mapping of multiple ontologies.

User rights and versioning workflows allow for ontology evolution.

Registries and repositories allow for discovery and access.


How can they be networked? Powerful visualisation tools allow for easy

understanding of the model:


How can they be networked? Cross-mapping tools allow for easy networking

and extension of multiple ontologies:

My ontology Y(our) ontology

mapping rules


How can they be applied? Semantically-aware applications can

understand OWL models out of the box.

Attached to databases they become a “knowledge base”, able to perform primitive inductive and deductive reasoning.

Other advantages can be query disambiguation as well as synonym, hyponym and multi-lingual searches


Examples

SPECIES

FAMILY

Merluccius

WATERAREAArea 27

Area 34 Area 37

includes species

is found in

Merlucciusmerluccius

Merlucciusaustralis

Area 41

Area 87Area 81


Knowledge base creation...

synonym expansion

fish stocks

species water areas

AgroVoc

commodities

user

geo-spatial data

document repositoriestime-series statistics

economic reports

Information systems mapped to ontologies.


NeOn project overview

NUTS AND BOLTS Timeline

Began in March 2006. Ends Feb 2010.

Funding EC funded, 14.3 million euros.

Members 13 project members, 6 languages... Universities, IT companies, National institutes, International orgs.


NeOn project objectives

Build a complete solution for the ontology lifecycle, including: Methods

A work package dedicated to creating methodologies for building ontologies

Models New models to permit multiple networked ontologies

Software An open-source, extensible software platform

Services Ontology repository Ontology registry

User community A work package dedicated to dissemination


NeOn project status

Project is at mid-life:

First RTMS-based ontos May 2007 First version of toolkit June 2007 Toolkit w/OWL support Spring 2008 Networked ontos Feb 2009 Demo Fisheries system Feb 2009


References...

NeOn project http://www.neon-project.org

NeOn toolkit http://www.neon-toolkit.org

Fisheries ontologies (first prototypes) http://www.fao.org/aims/neon.jsp

Documents

06-2008 Y. Jaques Yves Jaques ICIS Requirements Gathering, June 2008, Rome NeOn Lifecycle Support for Networked Ontologies