A Collaborative Framework Supporting Ontology Development Based on Agile and Scrum Model

Akkharawoot TAKHOM  Sasiporn USANAVASIN  Thepchai SUPNITHI  Prachya BOONKWAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.12   pp.2568-2577
Publication Date: 2020/12/01
Publicized: 2020/09/04
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDP7041
Type of Manuscript: PAPER
Category: Software Engineering
collaborative activities,  ontology development,  standard,  agile,  scrum,  process engineering,  

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Ontology describes concepts and relations in a specific domain-knowledge that are important for knowledge representation and knowledge sharing. In the past few years, several tools have been introduced for ontology modeling and editing. To design and develop an ontology is one of the challenge tasks and its challenges are quite similar to software development as it requires many collaborative activities from many stakeholders (e.g. domain experts, knowledge engineers, application users, etc.) through the development cycle. Most of the existing tools do not provide collaborative feature to support stakeholders to collaborate work more effectively. In addition, there are lacking of standard process adoption for ontology development task. Thus, in this work, we incorporated ontology development process into Scrum process as used for process standard in software engineering. Based on Scrum, we can perform standard agile development of ontology that can reduce the development cycle as well as it can be responding to any changes better and faster. To support this idea, we proposed a Scrum Ontology Development Framework, which is an online collaborative framework for agile ontology design and development. Each ontology development process based on Scrum model will be supported by different services in our framework, aiming to promote collaborative activities among different roles of stakeholders. In addition to services such as ontology visualized modeling and editing, we also provide three more important features such as 1) concept/relation misunderstanding diagnosis, 2) cross-domain concept detection and 3) concept classification. All these features allow stakeholders to share their understanding and collaboratively discuss to improve quality of domain ontologies through a community consensus.