@conference {ElIdrissi2022,
	title = {On The Formalization of The TOGAF Content MetaModel Using Ontologies},
	booktitle = {2022 International Conference on Intelligent Systems and Computer Vision, ISCV 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {Ontology, when well designed, provides a semantic model that facilitates human interpretation of models and reduces their ambiguity and complexity. A formal-based standards ontology can be processed automatically by computer programs, providing several benefits such as automatic knowledge discovery and inference, inconsistency detection, and interoperability. Several frameworks have been developed to control the complexity of the enterprise architecture. This paper focuses on the TOGAF framework and precisely on its content metamodel. It analyses and discusses the reported approaches to its formalization using ontologies. Additionally, it proposes some possible future directions to improve this work and enhance its adoption by the industrial world and academic communities. {\textcopyright} 2022 IEEE.},
	keywords = {Automatic knowledge discoveries, enterprise architecture, formalisation, Inconsistency detection, Interpretation of models, Meta model, Ontology, Ontology{\textquoteright}s, Semantic modelling, Semantic Web, Semantic-Web, Togaf framework},
	doi = {10.1109/ISCV54655.2022.9806063},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134300854\&doi=10.1109\%2fISCV54655.2022.9806063\&partnerID=40\&md5=6066972d0de16301bf7195b5ec177385},
	author = {El Idrissi, B. and Tetou, C. and Doumi, K.}
}
@article {ElIdrissi20227604,
	title = {RDF/OWL storage and management in relational database management systems: A comparative study},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	volume = {34},
	number = {9},
	year = {2022},
	note = {cited By 0},
	pages = {7604-7620},
	abstract = {This paper details RDF/OWL storage and management in two popular Relational Database Management Systems (RDBMSs): Oracle and Virtuoso. Popularity, sustainability, and conformance with the SPARQL language are the main reasons for choosing these systems. This work combines empirical and analytical studies guided by a comparative framework developed and motivated in the paper. Several dimensions are considered, including RDF data type preservation, SPARQL query and update processing, reasoning capabilities, custom inferences, blank node management, and other functional and non-functional features. Furthermore, a review of the performance assessments reported in the literature has been conducted. This study{\textquoteright}s results identify the advantages and shortcomings of these RDBMSs for storing and managing RDF/OWL. They can help improve these systems or serve as a guide in choosing an appropriate system to use in a project context. Opportunities for further research efforts are also suggested. {\textcopyright} 2021 The Authors},
	doi = {10.1016/j.jksuci.2021.08.018},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118134675\&doi=10.1016\%2fj.jksuci.2021.08.018\&partnerID=40\&md5=29bdbdbd9af8a67734ae88e4139656f5},
	author = {El Idrissi, B. and Baina, S. and Mamouny, A. and Elmaallam, M.}
}
@conference {ElIdrissi2016,
	title = {Bringing flexibility to ontology learning from relational databases},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	volume = {2016-July},
	year = {2016},
	note = {cited By 0},
	abstract = {Current approaches for learning ontology from relational databases are focused on the implementation of the transformation rules without paying attention to user requirements in terms of the way in which the ontology should be designed. They propose ready-made functions that transform the database model to an ontology TBox. There is no way to customize the learning process. The paper proposed a solution based on the transformation of the database model into an XML document, to allow its extension with some meta-data that guide the learning program on how to perform the transformation to ontology. Some case studies demonstrate the effectiveness and efficiency of the proposed approach in terms of reducing the risk that the produced ontology does not fit the requirements of the user. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/AICCSA.2015.7507213},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980327629\&doi=10.1109\%2fAICCSA.2015.7507213\&partnerID=40\&md5=e1fa4f3fc7cc38f9c811349f60c0660c},
	author = {El Idrissi, B. and Baina, S. and Baina, K.}
}
@conference {ElIdrissi2016,
	title = {Supporting collaborative work on ontology learning from Relational Databases},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	volume = {2016-July},
	year = {2016},
	note = {cited By 0},
	abstract = {We present in this paper, one important feature of our approach that draws on a semantic enrichment process of the RDB model for learning application ontologies from Relational Databases (RDB)s. In fact, our approach supports collaborative work on the enrichment process and considers the partitioning of the input RDB model into a set of business modules. The objectives behind the collaborative support are threefold: (i) to allow each user to work on the RDB part that fits his/her domain knowledge; (ii) to save time in manual semantic enrichment; and (iii) to make easiest human evaluation of the ontology. We present the principle behind our approach, its features and its benefits. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/AICCSA.2015.7507133},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980315537\&doi=10.1109\%2fAICCSA.2015.7507133\&partnerID=40\&md5=6c845ad8d51b40c22dba58c3aa06c4b7},
	author = {El Idrissi, B. and Baina, S. and Baina, K.}
}
@article {ElIdrissi2015235,
	title = {Ontology learning from relational database: How to label the relationships between concepts?},
	journal = {Communications in Computer and Information Science},
	volume = {521},
	year = {2015},
	note = {cited By 0},
	pages = {235-244},
	abstract = {Developing ontology for modeling the universe of a Relational Database (RDB) is a key success for many RDB related domains, including semantic-query of RDB, Linked Data and semantic interoperability of information systems. However, the manual development of ontology is a tedious task, error-prone and requires much time. The research field of ontology learning aims to provide (semi-) automatic approaches for building ontology. However, one big challenge in the automatic transformation, is how to label the relationships between concepts. This challenge depends heavily on the correct extraction of the relationship types. In fact, the RDB model does not store the meaning of relationships between entities, it only indicates the existence of a link between them. This paper suggests a solution consisting of a meta-model for the semantic enrichment of the RDB model and of a classification of relationships. A case study shows the effectiveness of our approach. {\textcopyright} Springer International Publishing Switzerland 2015.},
	doi = {10.1007/978-3-319-18422-7_21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929459677\&doi=10.1007\%2f978-3-319-18422-7_21\&partnerID=40\&md5=ac949fcc959bce20c507351a6259639e},
	author = {El Idrissi, B. and Baina, S. and Baina, K.}
}
@conference {ElIdrissi2013,
	title = {Automatic generation of ontology from data models: A practical evaluation of existing approaches},
	booktitle = {Proceedings - International Conference on Research Challenges in Information Science},
	year = {2013},
	note = {cited By 0},
	abstract = {Nowadays, ontology as a knowledge sharing approach plays an important role in semantic interoperability of enterprise applications (EAs). However, the manual process of ontology construction requires deep understanding of the domain. This approach is difficult, expensive and time-consuming. To overcome the knowledge acquisition bottleneck, ontology learning field aims to provide automatic and semi-automatic approaches for ontology generation. Several approaches have been emerged for this purpose. In this paper, we present a practical study of methods that take data models as input to the learning process. The main contributions of this work are: (i) the evaluation of the availability of existing approaches for (semi-)automatic generation of ontology from data models; (ii) the evaluation of tools according to their operability; and (iii) the evaluation of the resulting ontologies to assess their quality in supporting semantic interoperability. Our goal through this study is to find a response to the question: Is there a tool that extracts (semi-)automatically an application ontology from data models, intended for use in semantic interoperability?. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/RCIS.2013.6577694},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884153525\&doi=10.1109\%2fRCIS.2013.6577694\&partnerID=40\&md5=09ec241e846cc2376db979b7aeff7c68},
	author = {El Idrissi, B. and Baina, S. and Baina, K.}
}
