A knowledge-based model for managing the ontology evolution: case study of maintenance in SONATRACH

Abstract

The challenges of the development of a suitable ontology scheme in decision-making environment should be taken in conjunction with the exploitation of more recent technologies. It is expected that the use of ontologies will lead to the construction of more intelligent applications, allowing them to work more specifically at a human conceptual level. We propose in this article an approach that analyses the impact of changes in the ontology on business rules in order to detect inconsistencies that may be generated. In addition, the developed tool provides solutions to repair inconsistencies with the help of domain experts. In our work, business rules are edited from the concepts and properties that are stored in an OWL (Web Ontology Language) ontology named OntoloG. This latter is implemented throughout the use of Protégé 4.0.2.with the OWL sub-language. OntoloG has been developed by the knowledge acquisition from documents, collection and capitalisation of business rules process with experts in SONATRACH AVAL.

A systematic review on time-constrained ontology evolution in predictive maintenance

“…Six works describe and apply graph transformations to enact the evolutionary process (Tsalapati et al 2017;Shaban-Nejad and Haarslev 2015;Grandi 2016;Zheleznyakov et al 2019;Mahfoudh et al 2015) and six others provide insights in how to deal with consistency issues that may arise when evolutionary action is taken (Peixoto et al 2016;Bayoudhi et al 2017;Wang et al 2015;Touhami et al 2015;Gaye et al 2015;Sad-Houari et al 2019). Of these, one (Mahfoudh et al 2015) focuses on checking consistency before applying the changes.…”

“…Finally, twelve works offer insights about in which ways an ontology can be changed (Kondylakis and Papadakis 2018;Shaban-Nejad and Haarslev 2015;Peixoto et al 2016;Grandi 2016;Bayoudhi et al 2017;Wang et al 2015;Mahfoudh et al 2015;Touhami et al 2015;Gaye et al 2015;Sad-Houari et al 2019;Benomrane et al 2016). These studies assess and categorize the possible evolutionary actions that can be performed, materializing them into simpler, executable operations.…”

“…In regards to knowledge acquisition, data about failures and equipment status is often provided through documental sources (e.g. maintenance reports or manuals) (Sad-Houari et al 2019;Wang et al 2019;Cho et al 2019;Smoker et al 2017;Delgoshaei et al 2017), existing systems (e.g. Enterprise Resource Planning and Manufacturing Execution Systems) (Dibowski et al 2016;Bayar et al 2016;Cho et al 2019;Delgoshaei et al 2017), sensors (Dibowski et al 2016;Saeed et al 2019;Cho et al 2019;Klusch et al 2015;Steinegger et al 2017;Ferrari et al 2017), or combinations of these.…”

“…In these cases, the representation of time is done exclusively through time points (Cho et al 2019;Delgoshaei et al 2017;Saeed et al 2019;Klusch et al 2015;Ferrari et al 2017), and no approaches explicitly using time-constrained relationships have been found. Finally, half of all retrieved studies propose or show potential combination of ontologies with other methods in order to achieve better results in the identification of events and potential failures, such as machine learning algorithms (Cho et al 2019;Smoker et al 2017;Steinegger et al 2017), multi-agent systems (Steinegger et al 2017), rules (Sad-Houari et al 2019;Dibowski et al 2016;Bayar et al 2016), case-based reasoning (Ansari et al 2019) and probabilistic models (Klusch et al 2015;Ferrari et al 2017).…”

“…The impact of ontology evolution in existing rules is approached in Sad-Houari et al (2019). A change in an ontology may render deployed rules in engines or Business Rules Management Systems unusable, or even make these produce unexpected outcomes when triggered.…”