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

FAO-FIES Y. Jaques What are ontologies? networks of concepts and relationships taxonomies (parent/child) are just one type of relationship that can be modeled includes order includes family includes species

FAO-FIES Y. Jaques 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.

FAO-FIES Y. Jaques 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 Centrolophus niger SPECIES FAMILY includes species Rudderfish has name THING CEO has FAO alpha code

FAO-FIES Y. Jaques Ontologies are very expressive relationships can be constrained in many ways:

FAO-FIES Y. Jaques 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)

FAO-FIES Y. Jaques 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.

FAO-FIES Y. Jaques How can they be networked? Powerful visualisation tools allow for easy understanding of the model:

FAO-FIES Y. Jaques How can they be networked? Cross-mapping tools allow for easy networking and extension of multiple ontologies: My ontology Y(our) ontology mapping rules

FAO-FIES Y. Jaques 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

FAO-FIES Y. Jaques Examples SPECIES FAMILY Merluccius WATER AREA Area 27 Area 34 Area 37 includes species is found in Merluccius merluccius Merluccius australis Area 41 Area 87 Area 81

FAO-FIES Y. Jaques Knowledge base creation... synonym expansion fish stocks species water areas AgroVoc commodities user geo-spatial data document repositories time-series statistics economic reports Information systems mapped to ontologies.

FAO-FIES Y. Jaques NeOn project overview NUTS AND BOLTS Timeline Began in March Ends Feb Funding EC funded, 14.3 million euros. Members 13 project members, 6 languages... Universities, IT companies, National institutes, International orgs.

FAO-FIES Y. Jaques 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

FAO-FIES Y. Jaques NeOn project status Project is at mid-life: First RTMS-based ontosMay 2007 First version of toolkit June 2007 Toolkit w/OWL supportSpring 2008 Networked ontosFeb 2009 Demo Fisheries systemFeb 2009

FAO-FIES Y. Jaques References... NeOn project NeOn toolkit Fisheries ontologies (first prototypes)