Elsevier

Data & Knowledge Engineering

Volume 116, July 2018, Pages 138-158
Data & Knowledge Engineering

How to Repair Inconsistency in OWL 2 DL Ontology Versions?

https://doi.org/10.1016/j.datak.2018.05.010Get rights and content

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 protégé plugin was implemented to validate our approach.

Introduction

Domain knowledge capture has always been of crucial interest for humans and computer systems in several fields. Semantic formalisms such as ontologies allow holding this knowledge as well as sharing and understanding data. They also guarantee data interoperability in heterogeneous environments. However, knowledge is in continuous change in line with the evolving real-life requirements. Thus, an ontology has to follow such evolution and change accordingly. This process, known as ontology evolution, is “the process of managing ontology changes by preserving the consistency of the ontology with respect to a given notion of consistency” [1]. This definition reveals that an ontology consistency management is an important task in the ontology evolution process [2,3]. Ontology is commonly called consistent if it is free of logical contradictions [4,5]. It is equally called inconsistent if it violates the syntactical constraints of the language [6] or knowledge modeling guidelines [7,8].

To address the different types of inconsistencies, several approaches and tools have so far been designed [9]. Nevertheless, most of them cope with inconsistency at a late stage of the ontology engineering process. Indeed, inconsistency is checked and repaired in an a posteriori way. This is carried out either after the effective application of changes [4,5,7,10] or by relying on already adapted, stored ontology versions [8,11]. According to such a posteriori consistency checking approaches, inconsistency has already propagated to different dependent artifacts (i.e., parts of ontology, related ontologies and applications). Hence, the modeled knowledge becomes meaningless until an ontology repair action is undertaken. At this stage, many rolling backs may have to be conducted so as to restore the ontology to its last consistent state.

This paper addresses these problems by providing a broad view of consistent OWL 2 DL ontology evolution, where the inconsistencies that can arise due to changes on ontology are not only restricted to logical inconsistencies, but also include syntactical invalidities or style issues. An a priori consistency checking approach is adopted. To this end, change kits are defined and formalized to anticipate the different inconsistency types at an early stage. These formalisms are applied to a consistent OWL 2 DL ontology and anticipate potential inconsistencies due to a requested change in order to prevent their occurrence. Hence, change kits provide a means for implementing the change while preserving the ontology consistency. As a consequence, they minimize expert interventions or any rolling back for checking and restoring consistency. A prototype tool is also implemented to support knowledge engineers in preserving a consistent state of an ontology upon each attempted change. It inspects the ontology changes before they are applied, issues appropriate feedback and possibly suggests alternatives.

The remaining of this paper is structured as follows. Section 2 provides a background about this work by describing the OWL 2 DL ontology and presenting the considered consistency rules. Section 3 sheds light on the state of the art related to the ontology evolution approaches and tools. Section 4 presents the proposed inconsistency anticipation approach. Particularly, it characterizes the main concepts of an OWL 2 DL change kit in a formal way. Section 5 describes the developed tool and shows an illustrative example of its use. In Section 6, we describe the conducted experiments and analyze the obtained results before concluding this work.

Section snippets

Background and Motivations

The ontology evolution activity depends heavily on the ontology model as well as on the considered consistency criteria [7]. In the following subsections, the OWL 2 DL ontology language as well as the predefined consistency rules are described.

State of the Art

Ontology evolution is an engineering activity that was given different technical definitions which have recently been gathered in Ref. [9]. However, most of works share the definition of [7] which emphasizes the importance of the ontology consistency preservation task while applying changes. Despite this agreement, each approach has targeted a particular ontology language. For instance, KAON ontology language was considered in Ref. [6], OWL-Lite in Ref. [7], RDFS in Ref. [12], OWL DL in Refs. [

Overview of the Proposed Approach

The proposed approach is characterized by the ontology evolution process presented in Fig. 1.

A possible scenario of this process consists in:

  • 1.

    A domain expert requests a domain knowledge change.

  • 2.

    An OWL 2 expert downloads the current consistent domain ontology version to adapt it to this change.

  • 3.

    The OWL 2 expert represents this change (Ch) using the OWL 2 vocabulary and axioms [23].

  • Ch consists in adding or deleting an OWL 2 axiom.

Ch=AddChDelCh={Add_AxAxi/AxiA}{Del_AxAxi/AxiA};
  • 4.

    A consistent OWL 2

The OWLChangeKits Presentation

By analyzing the state of the art tools in section 2, we concluded that a new tool is still needed to anticipate inconsistency at runtime. It has to provide instantaneous feedbacks for users. This interaction functionality lies in explaining the reason which prevents the effective application of the requested change and suggests a corrective change. A new tool is required to minimize an expert intervention in the ontology evolution process and preserve the ontology consistency throughout its

Performance Evaluation and Discussion

In order to evaluate the latency of the developed tool, we applied consecutively the four change kits (described in Section 5.2) on three consistent SWRC ontology versions varying in their sizes and expressivities (See Table 4). We measured the time taken by tool to issue a feedback when a potential inconsistency is detected (see Table 5). We also measured the total execution time of each of the four change kits (see Table 6). The execution times were measured as the average over 4 runs for

Conclusion

An Ontology evolution is a vital activity in the ontology lifecycle because real-world changes are unavoidable. However, the ontology update should never affect its consistency. Most of existing approaches cope with the ontology consistency management issue at a late stage of the ontology lifecycle. Occasionally, experts intervene to check consistency and fix detected issues. Meanwhile, the ontology inconsistency has propagated to the dependent artifacts. Hence, the modeled knowledge becomes

Eng. Leïla Bayoudhi is currently a PhD student in computer systems engineering in the MIRACL laboratory (Multimedia, InfoRmation Systems and Advanced Computing Laboratory) from the University of Sfax, Tunisia. She got her computer engineer's degree in 2012 from the National Engineering School of Sfax, Tunisia. Her research work focuses on ontology engineering.

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    Eng. Leïla Bayoudhi is currently a PhD student in computer systems engineering in the MIRACL laboratory (Multimedia, InfoRmation Systems and Advanced Computing Laboratory) from the University of Sfax, Tunisia. She got her computer engineer's degree in 2012 from the National Engineering School of Sfax, Tunisia. Her research work focuses on ontology engineering.

    Dr. Najla Sassi is an assistant professor in Computer Science at the College of Computer Science and Engineering, Taibah University, KSA. She received her Master’s degree in computer science from the University of Rouen, France in 2004 and a PhD degree from the University of Sfax in 2011. Her main interests are Artificial Intelligence, Ontology and Information Systems Modelling. Dr. Najla Sassi was involved in several projects and has published several research papers in international journals and conferences.

    Wassim Jaziri received his PhD degree in Computer Science in 2004 from INSA-Rouen, France. He received an Accreditation to supervise research (French HDR, a required grade to be a full Professor) in Computer Science in 2010 from Sfax University-Tunisia. Currently, He is Professor in Computer Science at the College of Computer Science and Engineering, Taibah University, KSA. His main interests are Geographic Information Systems, Spatio-temporal Databases, Spatial Decision Aid, Data and Knowledge Modelling, Ontology and Optimization.

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