Ontology Based Requirements Analysis: Lightweight Semantic Processing Approach

Kaiya, Haruhiko; Saeki, Motoshi

doi:10.1109/qsic.2005.46

Cited by 101 publications

(60 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also defined several relationships including inheritance links, instantiation, and constraints. In [16] five types of concepts have been proposed to represent the functional requirements (function, object, and environment) and nonfunctional requirements (constraints, quality).…”

Section: Ontology Meta-modelmentioning

confidence: 99%

Improving Business Process Model Quality Using Domain Ontologies

2013

View full text Add to dashboard Cite

This paper addresses the issue of improving quality of business process (BP) models by exploiting domain knowledge. Indeed, business process models reflect the business processes of companies. The success of these processes has a direct and undeniable impact on business operations success. Managing them through their underlying models helps improving their effectiveness, consistency, and transparency. BP modeling aims at a better understanding of processes, allowing deciders to achieve strategic goals of the company. However, several studies from the literature showed that in experienced system analysts often produce low-level quality. This situation is partly due to lack of domain knowledge. In this paper, we propose to support this modeling effort with an approach that uses domain knowledge to improve the semantic quality of BP models. We suggest to use ontologies as a mean to capture domain knowledge and meta-modeling techniques to deal with BP models independently of languages in which they are expressed. Our contribution is threefold: (1) the meta-models describing both a domain ontology and a BP model are described, (2) the alignment between the concepts of both meta-models is defined and illustrated, (3) a set of Object Constraint Language mapping rules is provided. A simple case study illustrates the process.

show abstract

Section: Ontology Meta-modelmentioning

confidence: 99%

Improving Business Process Model Quality Using Domain Ontologies

2013

View full text Add to dashboard Cite

show abstract

“…To define the list of terms in an early phase, extracting domain-specific keywords automatically from the requirements documents was considered in [3], [4]. [5]- [7] considered ways to construct domain knowledge from the information extracted from the texts as an ontology describing the relationships among entities, and this knowledge was used to identify inconsistencies in the requirements documents.…”

Section: Related Workmentioning

confidence: 99%

Predicate Argument Structure Analysis for Use Case Description Modeling

Takeuchi

Nakamura

Yamaguchi

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYIn a large software system development project, many documents are prepared and updated frequently. In such a situation, support is needed for looking through these documents easily to identify inconsistencies and to maintain traceability. In this research, we focus on the requirements documents such as use cases and consider how to create models from the use case descriptions in unformatted text. In the model construction, we propose a few semantic constraints based on the features of the use cases and use them for a predicate argument structure analysis to assign semantic labels to actors and actions. With this approach, we show that we can assign semantic labels without enhancing any existing general lexical resources such as case frame dictionaries and design a less language-dependent model construction architecture. By using the constructed model, we consider a system for quality analysis of the use cases and automated test case generation to keep the traceability between document sets. We evaluated the reuse of the existing use cases and generated test case steps automatically with the proposed prototype system from real-world use cases in the development of a system using a packaged application. Based on the evaluation, we show how to construct models with high precision from English and Japanese use case data. Also, we could generate good test cases for about 90% of the real use cases through the manual improvement of the descriptions based on the feedback from the quality analysis system. key words: use case, modeling, predicate argument structure, semantic constraint

show abstract

“…They adapted the requirements analyses introduced by Kaiya [12] to boilerplates requirements and added a new analysis called opacity. The requirement language used in this work are based upon their combination of boilerplates and the domain ontology.…”

Section: Pattern-based Requirementsmentioning

confidence: 99%

Ontology-Driven Guidance for Requirements Elicitation

Farfeleder

Moser

Krall

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Requirements managers aim at keeping their sets of requirements well-defined, consistent and up to date throughout a project's life cycle. Semantic web technologies have found many valuable applications in the field of requirements engineering, with most of them focusing on requirements analysis. However the usability of results originating from such requirements analyses strongly depends on the quality of the original requirements, which often are defined using natural language expressions without meaningful structures. In this work we present the prototypic implementation of a semantic guidance system used to assist requirements engineers with capturing requirements using a semi-formal representation. The semantic guidance system uses concepts, relations and axioms of a domain ontology to provide a list of suggestions the requirements engineer can build on to define requirements. The semantic guidance system is evaluated based on a domain ontology and a set of requirements from the aerospace domain. The evaluation results show that the semantic guidance system supports the requirements engineer in defining well-structured requirements.

show abstract

Ontology Based Requirements Analysis: Lightweight Semantic Processing Approach

Cited by 101 publications

References 13 publications

Improving Business Process Model Quality Using Domain Ontologies

Improving Business Process Model Quality Using Domain Ontologies

Predicate Argument Structure Analysis for Use Case Description Modeling

Ontology-Driven Guidance for Requirements Elicitation

Contact Info

Product

Resources

About