Evolving ontologies using an adaptive multi-agent system based on ontologist-feedback

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

“…Three works focus on the identification of candidate evolutionary actions (Kondylakis and Papadakis 2018;Grandi 2016;Osborne and Motta 2018;Benomrane et al 2016). Of these, one (Osborne and Motta 2018) aims to ease this step by allowing the user to specify requirements for new candidates and another (Benomrane et al 2016) allows an ontologist to select actions they want to see executed.…”

“…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.…”

“…Li et al (2015), Algosaibi and Melton (2016), Ziembinski (2016) identify metrics that can be employed to measure changes between ontology versions. The retrieved results show there are two main comparison methods employed: comparing the ontology's elements to (new or existing) corpora in order to identify potential new terms, or terms that may be becoming irrelevant (Benomrane et al 2016;Cardoso et al 2018;Cano-Basave et al 2016;Ziembinski 2016); and comparing different versions of the same ontology by analyzing the changes in its internal structure (Zhang et al 2016;Duque-Ramos et al 2016;Stavropoulos et al 2019;Li et al 2015;Algosaibi and Melton 2016). Table 9 displays a set of intrinsic metrics that can be obtained by analysing the ontology's internal structure, while Table 10 shows metrics related to extrinsic properties (i.e., obtained through the analysis of data sources to which the ontology can be applied, and not to the ontology itself).…”

“…When it comes to the methods using to identify new candidates and to analyze evolutionary trends, Natural Language Processing and Machine Learning algorithms have been popularly used to prospect text and identify potential changes for the ontology. Of the retrieved studies, they are explicitly employed in Benomrane et al (2016), Cardoso et al Bayoudhi et al (2017) Change to Object Property Bayoudhi et al (2017), Mahfoudh et al (2015) Add Equivalent Class Bayoudhi et al (2017), Mahfoudh et al (2015) Add Disjoint Class Bayoudhi et al (2017), Mahfoudh et al (2015) Remove Equivalent Class Bayoudhi et al (2017), Mahfoudh et al (2015) Remove Disjoint Class Bayoudhi et al (2017), Mahfoudh et al (2015) Add Equivalent Object Property Bayoudhi et al (2017), Mahfoudh et al (2015) Remove Equivalent Object Property Bayoudhi et al (2017), Mahfoudh et al (2015) Add Disjoint Object Property Bayoudhi et al (2017), Mahfoudh et al (2015) Remove Disjoint Object Property Peixoto et al ( 2016), Touhami et al (2015) Change (preferred) Label Mahfoudh et al (2015) Add Individual Mahfoudh et al (2015) Remove Individual Mahfoudh et al (2015) Rename Individual (2018), Cano-Basave et al ( 2016), Stavropoulos et al (2019), Ziembinski (2016). A summary of the applied algorithms can be found in Table 11.…”

“…A summary of the applied algorithms can be found in Table 11. Furthermore, of the retrieved works, two (Benomrane et al 2016;Zhang et al 2016) employ Multi-Agent Systems to describe the ontology and potential changes.…”