Applications of ontologies in requirements engineering: a systematic review of the literature

Dermeval, Diego; Vilela, Jéssyka; Bittencourt, Ig Ibert; Castro, Jaelson; Isotani, Seiji; Brito, Patrick H. S.; Silva, Alan

doi:10.1007/s00766-015-0222-6

Cited by 144 publications

(95 citation statements)

References 34 publications

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“…The purpose of using SLR is to present a coherent understanding on how scientific ontologies are able to support RE [3]. Studies have also been done on scientific ontologies and some of the most prominent ones are highlighted briefly in the next paragraphs.…”

Section: Current State Of the Art And Related Work On Re And Somentioning

confidence: 99%

“…Furthermore, they are capable of analysing the structure of the body of knowledge which generates a common definition of knowledge. On the same note, conceptualisation has been represented as an abstract or an item within a discipline that is represented in the semantic web [3]. Different perceptions on how the integration of Scientific Ontologies (SO) has been incorporated in RE have pointed to the following aspects: Being able to solve some specified problems, management of knowledge in RE, improvement of the RE specifications as well as being able to perform analysis processes in RE [3].…”

Section: Introductionmentioning

confidence: 99%

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Onto-Engineering: A Conceptual framework for Integrating Requirement Engineering Process with scientifically tuned Digital Forensics Ontologies

Kebande¹,

Maake²

2017

IJCSDF

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A framework for integrating Requirement Engineering (RE) with scientifically tuned Digital Forensics Ontologies (SDFO) envisages a semantic web-driven approach that is able to provide a shared understanding of unifying RE techniques coupled with digital investigation techniques that are tuned from an ontological perspective. In the context of this paper, RE has been portrayed as a discipline that can not only be able to validate, specify, analyse and provide elicitation of the requirements but also to manage them effectively. Nevertheless, SDFO have been employed as bodies of knowledge that provides a shared understanding of knowledge or discipline within the Digital Forensic (DF) domain that helps to solve some given problems. Mainly, this requires the mapping/integrating of RE processes to DF tuned ontologies. The objective of the work presented in this paper, therefore, is to show how RE can be integrated into SDFO with the aim of identifying the most effective scientific approaches using an OntoTuning Matcher (OTM) that has been proposed in this paper. This paper was able to represent a scientifically tuned approach using the Automatic Semantic Mapping of Ontologies (ASMOV) approach. ASMOV provides an approach that is able to align ontologies with other systems such that inconsistencies are eliminated and the accuracy is increased. The contribution of the paper is presented in two folds: Firstly, the author identifies a high-level Onto-Engineered framework for integrating RE and SDFO, thereafter, a more detailed Onto-Engineered framework is discussed. The Onto-Engineered framework that has been discussed in this paper will help to clarify different diversification aspects that exists between RE and SDFO.

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Section: Current State Of the Art And Related Work On Re And Somentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Onto-Engineering: A Conceptual framework for Integrating Requirement Engineering Process with scientifically tuned Digital Forensics Ontologies

Kebande¹,

Maake²

2017

IJCSDF

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Many studies have explored the use of ontologies in requirement engineering [4,5]. Some studies adopted ontologies during the elicitation process to reduce ambiguous and incomplete requirements [4,5]. Other studies build ontologies for describing the structure of requirements specification documents in order to reduce the insufficient requirements specification [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies adopted ontologies during the elicitation process to reduce ambiguous and incomplete requirements [4,5]. Other studies build ontologies for describing the structure of requirements specification documents in order to reduce the insufficient requirements specification [4,5]. Furthermore, application domain ontologies have been used to represent the application domain knowledge and enable the reuse of the requirements for the applications of the same domain [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Derivation of Event-B Models from OWL Ontologies

Alkhammash

2016

MATEC Web Conf.

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Abstract. The derivation of formal specifications from large and complex requirements is a key challenge in systems engineering. In this paper we present an approach that aims to address this challenge by building formal models from OWL ontologies. An ontology is used in the field of knowledge representation to capture a clear view of the domain and to produce a concise and unambiguous set of domain requirements. We harness the power of ontologies to handle inconsistency of domain requirements and produce clear, concise and unambiguous set of domain requirements for Event-B modelling. The proposed approach works by generating Attempto Controlled English (ACE) from the OWL ontology and then maps the ACE requirements to develop Event-B models. ACE is a subset of English that can be unambiguously translated into first-order logic. There is an injective mapping between OWL ontology and a subset of ACE. ACE is a suitable interlingua for producing the mapping between OWL and Event-B models for many reasons. Firstly, ACE is easy to learn and understand, it hides the math of OWL and would be natural to use by everybody. Secondly ACE has a parser that converts ACE texts into Discourse Representation Structures (DRS). Finally, ACE can be extended to target a richer syntactic subset of Event-B which ultimately would facilitate the translation of ACE requirements to Event-B.

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Color image knowledge model construction based on ontology

Zhang

Yang

2019

Color Research & Application

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Color, as one of the most important visual features in a product system, can easily cause psychological and emotional resonances. Depending on the importance of user's emotional needs in the market and the complexity of a mining process, it can be reflected in the adjective description of color image. In this study, ontology combined with big data was used to mine the color image, a visual sense of user's psychological requirements. The results showed that the constructed color image ontology model could logically express the conceptual knowledge of color image. And this method solved problems that exist in current research on image mining and at the same time increased the accuracy of image information mining. Further examination of the color brand images was used to obtain the idea of “Stylish” and “Natural.” Finally, the acquisition of their physical form was named and surveyed to prove the feasibility of the research approach. This is a new attempt to apply ontology to the color image cognition process.

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Applications of ontologies in requirements engineering: a systematic review of the literature

Cited by 144 publications

References 34 publications

Onto-Engineering: A Conceptual framework for Integrating Requirement Engineering Process with scientifically tuned Digital Forensics Ontologies

Onto-Engineering: A Conceptual framework for Integrating Requirement Engineering Process with scientifically tuned Digital Forensics Ontologies

Derivation of Event-B Models from OWL Ontologies

Color image knowledge model construction based on ontology

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