OntoQualitas: A framework for ontology quality assessment in information interchanges between heterogeneous systems

An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

“…For evaluation, we discuss a series of aspects according to state-of-the-art ontology evaluation approaches [48,49].…”

“…existing gold standard ontologies, other model sources, etc.) [49]. Here, we compare AoFeCSO with a number of existing cloud (service) ontologies in terms of both the coverage scale and the details.…”

Ontologies in the context of product lifecycle management: state of the art literature review

“…Similarly, but with a different approach, Abdul-Ghafour (2009) proposes an approach based on the implementation of mediated ontologies and the construction of a common design features ontology, used as an interlingua for the exchange of product data. Also, Rico et al (2014) propose a framework supporting information interchange including an approach for evaluating the quality of an ontology. Lim et al (2010Lim et al ( , 2011) provide a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically related annotations based on design and manufacturing repository.…”

“…Ontology supports data integration through the application of Linked Data principles El Kadiri, Milicic, and Kiritsis (2013) Data/ information exchange Ontology encompasses the model and the data and thus can be seamlessly exchanged among different systems on a global basis based on standardised representation languages (OWL/RDF/ RDFS) Rico et al (2014), Dartigues (2003), Dartigues et al (2007), Abdul-Ghafour (2009), Lin and Harding (2007), and Yoo and Kim (2002) Information modelling Ontology enables information modelling of the product throughout its entire lifecycle and supports expressing a shared understanding of a domain as a common source of knowledge Barbau et al (2012), Vegetti, Henning, andLeone (2005), Giménez et al (2008), Panetto, Dassisti, andTursi (2012), Kiritsis (2011), Matsokis and Kiritsis (2010), Milicic et al (2012) and Perdikakis et al (2012) Knowledge engineering Ontology supports capturing, storing and retrieving knowledge taking advantages from reasoning mechanisms to infer hidden and tacit facts Sanya and Shehab (2014), Giménez et al (2008), Kiritsis (2011), Matsokis andKiritsis (2010), Lutzenberger, Klein, andThoben (2013), Panetto, Dassisti, and Tursi (2012), Chen, Chen, andChu (2009), Jiang, Peng, and, Lin and Harding (2007), Fortineau (2013), Milicic et al (2012aMilicic et al ( , 2013, and Grosse, Milton-Benoit, and Wileden (2005) International Journal of Production Research 7 models. This conversion results in semantic loss in information representation, which requires a preliminary knowledge of the conceptual model in order to operate on the database.…”

Towards natural language question generation for the validation of ontologies and mappings

“…More recently, Rico et al [ 14 ] presented the OntoQualitas framework. In this work, no new type of criterion addressing the quality of an ontology has been introduced, but the metrics to calculate them have been improved and refined.…”