Combining ontologies for requirements elicitation

Yuan, Xu; Tripathi, Shubhrendu

doi:10.1109/modre.2015.7343873

Cited by 8 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[103]- [105], [110], [117]- [128] 42 Questionnaires [21], [22], [27], [37]- [39], [43], [47], [48], [53]- [55], [70], [72]- [76], [79], [80], [82], [83], [85], [86], [88]- [91], [94]- [96], [98], [103]- [106], [110], [119], [122], [128]- [132] 33…”

Section: Elicitation Methodsmentioning

confidence: 99%

Elicitation Approaches and Techniques for Automating Business Processes

Menezes¹

2022

Preprint

View full text Add to dashboard Cite

<p>Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a literature review to discover methods for understanding business processes and eliciting requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges with methods for eliciting requirements in this context.</p>

show abstract

Section: Elicitation Methodsmentioning

confidence: 99%

Elicitation Approaches and Techniques for Automating Business Processes

Menezes¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Syntactically, ontology modeling usually uses OWL (Web Ontology Language) recommended by W3C. Moreover, OWL supports first-order logical reasoning based on SWRL, making OWL-based ontology modeling widely used in requirements engineering 9,10 .…”

Section: Scenario Ontology-based Requirements Extractionmentioning

confidence: 99%

Scenario-based requirements analysis for Chinese new train control system

Yuan

et al. 2022

International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

View full text Add to dashboard Cite

In order to meet the needs of railway operations in western China, the development of Chinese New Train Control System (CTCS-N) has been launched. To obtain complete and correct system requirements for CTCS-N early in system development, this paper applies the scenario-based requirements analysis method in software engineering to the requirements analysis of CTCS-N. This method takes the system operation scenario as input, analyzes the behaviors of the scenario based on ontology theory, and extracts the system requirements contained in the scenario based on SWRL (Semantic Web Rule Language) reasoning rules. Furthermore, the timed automata (TA) network model is constructed for requirement verification. Based on the design of the CTCS-N architecture, the above method is used to analyze the requirements based on the train integrity monitoring scenario as an example. The result shows that the method can effectively obtain the system requirements and provides a reference for the requirements development of CTCS-N.

show abstract

“…The work by Yuan and Tripathi (2015) proposes a methodology in which the knowledge domainencapsulatedinexistingontologiesiscombinedwithanontologydevelopedtocapturethe requirements,fromanelicitationprocess.Themaincontributionofthismethodologyisthatitallows reasoningthroughthecombinedontologies,withoutcreatingaseparatemergedontology.Themain disadvantageisthatitisdoneforaspecificdomain,forelicitationprocesses.Ontologymergingwithout generatinganewontologyisanotherapproachtocombinedifferentmappingsbetweenontologies withtheknowledgetobeintegrated,presentedbyAbbasandBerio(2013).…”

Section: Related Workmentioning

confidence: 99%

Procedure Based on Semantic Similarity for Merging Ontologies by Non-Redundant Knowledge Enrichment

Rangel

Altamiranda

Cerrada

et al. 2018

International Journal of Knowledge Management

View full text Add to dashboard Cite

The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good results are obtained in terms of completeness, relationships and properties, without creating redundancy.

show abstract

Combining ontologies for requirements elicitation

Cited by 8 publications

References 19 publications

Elicitation Approaches and Techniques for Automating Business Processes

Elicitation Approaches and Techniques for Automating Business Processes

Scenario-based requirements analysis for Chinese new train control system

Procedure Based on Semantic Similarity for Merging Ontologies by Non-Redundant Knowledge Enrichment

Contact Info

Product

Resources

About