Special issue on conceptual modeling - 34th International Conference on Conceptual Modeling (ER 2015)

Paul Johannesson; Mong Li Lee; Liddle, S.; Opdahl, A.; Pastor López, O. (2017). Special issue on conceptual modeling - 34th International Conference on Conceptual Modeling (ER 2015). Data & Knowledge Engineering. 109:1-2. doi:10.1016/j.datak.2017.03.001S1210

Domain ontology for digital marketplaces

Recently the sharing economy has emerged as a viable alternative to fulfilling a variety of consumer needs. As there is no consensus on the definition of ‘sharing economy’ we use the term ‘marketplace’ to refer more specifically to Internet/software-based sharing economy platforms connecting two different market segments. In the field of sharing economy and marketplaces we found a research gap concerning the (socio)technological aspects and the development of marketplaces. A marketplace ontology can help to have a clear account of marketplace concepts which will facilitate communication, consensus and alignment. In this paper we design this marketplace ontology in four steps. First the selection of UFO as foundation and UFO-S as core ontology. Second the search for a set of minimal conditions and properties common for marketplaces and the derivation into competency questions. Third, use the competency questions to identify fragmented sub-ontology pieces called Domain-Related Ontology Patterns (DROPs) and apply them informally by extending UFO-S concepts to design a marketplace domain ontology. This marketplace domain ontology is represented in OntoUML. The last step is the validation of the OntoUML model using expert knowledge

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of datasets, known as FAIR principles, were introduced in 2016 to enable machines to perform automatic actions on a variety of digital objects, including datasets. Since then, the principles have been widely adopted by data creators and users worldwide with the ‘FAIR’ acronym becoming a common part of the vocabulary of data scientists. However, there is still some controversy on how datasets should be interpreted since not all datasets that are claimed to be FAIR, necessarily follow the principles. In this research, we propose the OntoUML FAIR Principles Schema, as an ontological representation of FAIR principles for data practitioners. The work is based on OntoUML, an ontologically well-founded language for Ontology-driven Conceptual Modeling. OntoUML is a proxy for ontological analysis that has proven effective in supporting the explanation of complex domains. Our schema aims to disentangle the intricacies of the FAIR principles’ definition, by resolving aspects that are ambiguous, under-specified, recursively-specified, or implicit. The schema can be considered as a blueprint, or a template to follow when the FAIR classification strategy of a dataset must be designed. To demonstrate the usefulness of the schema, we present a practical example based on genomic data and discuss how the results provided by the OntoUML FAIR Principles Schema contribute to existing data guidelines

Boosting D3FEND: Ontological Analysis and Recommendations

Formal Ontology is a discipline whose business is to develop formal theories about general aspects of reality such as identity, dependence, parthood, truthmaking, causality, etc. A foundational ontology is a specific consistent set of these ontological theories that support activities such as domain analysis, conceptual clarification, and meaning negotiation. A (well-founded) core ontology specifies, under a foundational ontology, the central concepts and relations of a given domain. Foundational and core ontologies can be seen as ontology engineering frameworks to systematically address the laborious task of building large (more specific) domain ontologies. However, both in research and industry, it is common that ontologies as computational artifacts are built without the aid of any framework of this kind, favoring the occurrence of numerous modeling mistakes and gaps. Through a case study, here we show an exemplar of such a case in the domain of cybersecurity by providing an ontological analysis of D3FEND, an OWL knowledge graph of cybersecurity countermeasure techniques proposed by the MITRE Corporation. Based on the Reference Ontology for Security Engineering (ROSE), a core ontology of the security domain founded in the Unified Foundational Ontology (UFO), our investigation reveals a number of semantic deficiencies in D3FEND, including missing concepts, semantic overload of terms, and a systematic lack of constraints that renders that model under-specified. As a result of our ontological analysis, we propose several suggestions for the appropriate redesign of D3FEND to overcome those issues.</p

The Landscape of Ontology Reuse Approaches

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits

The many facets of trust

Trust is an attitude that an agent (the trustor) has toward an entity (the trustee), such that the trustor counts upon the trustee to act in a way that is benefi- cial w.r.t. to the trustor’s goals. The notion of trust is relevantly discussed both in in- formation science and philosophy. Unfortunately, we still lack a satisfying account for this concept. The goal of this article is to contribute to filling this gap. First, we take issue with some central tenets shared by the main philosophical accounts, such as that there is just one relation of trust, that this relation has three argument places, and that trust is reliance plus some extra factor. Second, we provide a novel account of trust, also discussing different levels of trust. According to the account we put forth here, the logical form of trust sentences is expressed by a four-place relation. Further, we distinguish and characterize four kinds of trust relations and their connections. We also argue that trust and reliance are different phenomena. Third, on the basis of the proposed account, we extend the Reference Ontology of Trust (ROT). We call the new version of ROT that includes this extension ”ROT 3.0”. Finally, we discuss the implications of the new ontological definitions in the applications we have done of the concept of trust in other works, also pointing out future applications made possible by these novel accounts of trust

An Ontology-Based multi-domain model in Social Network Analysis: Experimental validation and case study

The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model

An ontology-based multi-domain model in social network analysis: Experimental validation and case study

Benítez-Andrades, J. A., García-Rodríguez, I., Benavides, C., Alaiz-Moretón, H., & Labra Gayo, J. E. (2020). An ontology-based multi-domain model in social network analysis: Experimental validation and case study. Information Sciences, 540, 390-413. https://doi.org/10.1016/j.ins.2020.06.008[EN] The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model.SIJunta de Castilla y Leó