Najla Sassi

Approach and tool to evolve ontology and maintain its coherence

The concept of ontology is more and more used to provide a shared understanding of a domain of interest and to enhance communication among humans, computers and software. However, ontologies are often used in changing environments and, therefore, must be adapted to evolution requirements. This paper proposes an approach to manage the ontology evolution and to maintain its coherence after changing. This approach anticipates incoherencies that can be generated and proposes additional operations to correct them. To assist users in expressing and applying evolution requirements, an ontology evolution tool has been developed and applied to develop the Tunisian Education system.

Using Temporal Versioning and Integrity Constraints for Updating Geographic Databases and Maintaining Their Consistency

The use of geographic data has become a widespread concern, mainly within applications related to spatial planning and spatial decision-making. Therefore, changing environments require databases adaptable to changes that occur over time. Thus, supporting geographic information evolution is essential and extremely important within changing environments. The evolution is expressed in the geographic database by series of update operations that should maintain its consistency. This paper proposes an approach for updating geographic databases, based on update operators and algorithms of constraints integrity checking. Temporal versioning is used to keep the track of changes. Every version presents the state of the geographic database at a given time. Algorithms of constraints integrity checking allow maintaining the database consistency upon its update. To implement our approach and assist users in the evolution process, the GeoVersioning tool is developed and tested on a sample geographic database.

Overview and reflexion on OWL 2 DL ontology consistency rules

Domain knowledge capture has always been a major interest for both humans and computer systems. Thus, semantic formalisms, such as ontologies, become necessary to hold, share and understand that knowledge. As the world is continually changing, ontology must be updated accordingly. However, ontology changes should never affect its consistency as ontology needs to remain in a consistent state along its lifecycle. To this end, ontology consistency rules are defined and used to check not only logical inconsistencies but also syntactical invalidities and style issues. They are adopted from a posteriori approaches and adapted to enable an a priori detection/fixing of ontology inconsistencies. This paper discusses ontology inconsistency issues and describes the different consistency rules adopted from the literature. Then, it proves their useful adaptation for an a priori inconsistency detection and fixing.

A Hybrid Storage Strategy to Manage the Evolution of an OWL 2 DL Domain Ontology

Abstract Ontology is mainly used to capture “the knowledge semantics”. As knowledge is continually evolving over time, an ontology should evolve accordingly. Its previous states/versions should also be kept in order to allow the history access. Existing solutions for versions storage provide a trade-off between storage space efficiency and “knowledge semantics” keeping. Indeed, some storage strategies try to capture the evolving “knowledge semantics”, but unfortunately suffer from space overhead due to the storage of redundant information. Other strategies propose efficient storage solutions while losing the semantics of knowledge. These issues are addressed by our hybrid storage strategy which combines the benefits of the ontology and the database technologies. A reference ontology version is used to allow capturing the semantics along the whole ontology versioning process. A Temporal Object Oriented Database (TOODB) is used for storing and retrieving the ontology evolution history.

Supporting ontology adaptation and versioning based on a graph of relevance

Ontologies recently have become a topic of interest in computer science since they are seen as a semantic support to explicit and enrich data-models as well as to ensure interoperability of data. Moreover, supporting ontology adaptation becomes essential and extremely important, mainly when using ontologies in changing environments. An important issue when dealing with ontology adaptation is the management of several versions. Ontology versioning is a complex and multifaceted problem as it should take into account change management, versions storage and access, consistency issues, etc. The purpose of this paper is to propose an approach and tool for ontology adaptation and versioning. A series of techniques are proposed to ‘safely’ evolve a given ontology and produce a new consistent version. The ontology versions are ordered in a graph according to their relevance. The relevance is computed based on four criteria: conceptualisation, usage frequency, abstraction and completeness. The techniques to carry out the versioning process are implemented in the Consistology tool, which has been developed to assist users in expressing adaptation requirements and managing ontology versions.

How to Repair Inconsistency in OWL 2 DL Ontology Versions

Abstract Semantic modeling knowledge formalisms, such as ontologies, have to follow the continuous evolution and changes of knowledge. However, ontology changes should never affect its consistency. Ontology needs to remain in a consistent state along its whole engineering process. In the literature, most of approaches check/repair ontology inconsistencies in an a posteriori way. In this paper, an a priori inconsistency approach was proposed to generate consistent OWL 2 DL ontology versions. It relies on the OWL 2 DL change kits, which anticipate inconsistencies upon each change request on an ontology version. The proposed approach predicts potential inconsistencies, provides an a priori repair action and applies the required changes. Consistency rules were defined and used to check logical inconsistencies, but also syntactical invalidities and style issues. A protege plugin was implemented to validate our approach.

OWL 2 DL ontology inconsistencies prediction

When using ontology in dynamic environments, we should adapt it accordingly to follow the new requirements. Ontology should remain in a consistent state after changes. Otherwise, ontology inconsistency would be propagated to the dependent artifacts and may engender serious errors. This issue is addressed in this paper, by proposing an a priori repair action to prevent inconsistencies when updating OWL 2 DL ontologies. Predictive algorithms are defined to foresee the potential inconsistencies and to keep the ontology logically consistent, free of syntactical invalidities and style issues after each change.

Anticipatory Approach to Maintain Consistency in Ontology Versions

Ontologies are often used in changing environmentsand therefore cannot be considered as a finite and stableconceptualization of a domain of knowledge. The managementof ontology evolution requires monitoring and consideration of two important and complementary aspects: (1) the history of evolution to ensure traceability of changes and (2) the conformity of ontology in relation to their original objectives.This paper proposes an anticipatory approach for maintaining consistency of ontology after its evolution. This approach is based on the anticipation of inconsistencies that can be generated by types of change in order to propose additional functions to correct them. These functions called corrective operations aim to address the potential inconsistencies that are likely to be generated. Each type of change must be applied in conjunction with the corrective operations to guarantee theconformity and the quality of ontology. An Ontology Evolution Tool has been developed to allow updating ontologies while preserving their coherence.