Ontology Evolution for Experimental Data in Food

Abstract

Abstract. Throughout its life cycle, an ontology may change in order to adapt to domain changes or to new usages. This paper presents an ontology evolution activity [1] applied to an ontology dedicated to the annotation of experimental data in food [2], and a plug-in, DynarOnto, which assists ontology engineers for carrying out the ontology changes. Our evolution method is an a priori method which takes as input an ontology in a consistent state, implements the changes selected to be applied and manages all the consequences of those changes by producing an ontology in a consistent state.

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

“…For example, Touhami et al in [6] propose an ontology evolution activity that assists ontologists for carrying out the ontology changes. It lists all possible changes to ontologists, identifies all consistency constraints to be checked after applying each change.…”

“…Ontologies are designed to provide a formal and explicit representation of knowledge of a domain in terms of concepts (or classes), relations between these concepts and instances of these concepts [6]. The importance of ontologies is increasing especially in multi-agent environments since they enable agents to communicate, interact, and understand each other [2,6].…”

“…The importance of ontologies is increasing especially in multi-agent environments since they enable agents to communicate, interact, and understand each other [2,6].…”

“…Ontology evolution means modifying or upgrading the ontology when there is a certain need for change or there comes a change in the domain knowledge [1]. The current ontology evolution techniques such as in [5][6][7][8][9][10][19][20][21][22][23] have several hidden weaknesses such as automating ontology evolution and resolving inconsistencies after applying new changes. The automation is important because human intervention is time consuming and error prone.…”

Ontology evolution for an experimental data integration system

“…Our ontology evolution activity proposes an a priori approach to help domain experts of the ONDINE system to manage the naRyQ evolution while preserving its CC-coherence. It is represented in the workflow given in Figure 4 (Touhami et al, 2015), based on the methodological guidelines given in Palma et al (2012) and Haase and Stojanovic (2005).…”