Algebraic graph transformations for formalizing ontology changes and evolving ontologies

Abstract

a b s t r a c tAn ontology represents a consensus on the representation of the concepts and axioms of a given domain. This consensus is often reached through an iterative process, each iteration consisting in modifying the current version of the consensus. Furthermore, frequent and continuous changes are also occurring when the represented domain evolves or when new requirements have to be considered. Consequently, ontologies have to be adaptable to handle evolution, revision and refinement. However, this process is highly challenging as it is often difficult to understand all affected ontology parts when changes are performed. Thus, inconsistencies can occur in the ontology as the changes can introduce contradictory axioms. To address this issue, this paper presents a formal approach for evolving ontologies using Typed Graph Grammars. This method relies on the algebraic approach Simple PushOut (SPO) of graph transformations. It formalizes the ontology changes and proposes an a priori approach of inconsistencies resolution. The modified ontology does not need an explicit checking as an incorrect ontology version cannot actually be generated. To validate our proposal, an implementation is presented using the Attributed Graph Grammar (AGG) toolbox.

Efficient management and storage of a multiversion OWL 2 DL domain ontology

“…However, they were limited on lightweight ontologies or those that are represented in a non‐standard language (e.g., Rabbit in Denaux et al, ; KAON in Stojanovic, ; and OWL DL in Lösch, Rudolph, Vrandečić, & Studer, ). Mahfoudh et al () have addressed some simple inconsistency types in a priori way. To this end, they used the Graph Grammars to check a potential ontology inconsistency.…”

“…The former allows checking inconsistency before applying the change. It was adopted by few works (Denaux, Thakker, Dimitrova, & Cohn, 2012;Jaziri, Sassi, & Gargouri, 2010;Mahfoudh, Forestier, Thiry, & Hassenforder, 2015;Stojanovic, 2004) that are subject to some limitations. Indeed, they have just used lightweight ontologies or those that are represented in a non-standard language (e.g., Rabbit in Denaux et al, 2012;Karlsruhe Ontology (KAON) in Stojanovic, 2004; and the Unified Modeling Language (UML) in Jaziri et al, 2010).…”

Ontology Evolution for Experimental Data in Food

“…This type of approach allows the application of changes in order to evaluate the evolution impact on the ontology, then suggests how to repair inconsistencies in case of problems. To avoid backtracking after modification, resulting in a loss of time and resources, a priori approaches, where the coherence checking is made before the application of changes, have been proposed in the literature [9,10,16]. The work of Stojanovic [8] is the first to propose an ontology evolution process defined for KAON ontologies and using strategies for the task of managing changes.…”

“…A set of rules that must be maintained during the evolution of an ontology is defined but, as in [10], the authors don't define explicitly the type of coherence which is considered. More recently, the authors of [16] proposed a framework based on graph rewriting rules that maintains a set of constraints. However, their approach takes into account only one way to manage the inconsistencies after applying changes (e.g.…”

Ontology evolution for an experimental data integration system

“…Tissaoui et al (2011) present a system of evolution of an OTR dedicated to the semantic annotation of text documents. Mahfoudh et al (2015) propose a framework based on graph rewriting rules that maintains a set of constraints. In our work, we have combined, adapted and extented these existing approaches on ontology evolution in order to propose a naRyQ a priori evolution activity taking into account its main originality that is its inter dependent concepts to manage experimental data.…”