SUMMARYCreating an ontology from multidisciplinary knowledge is a challenge because it needs a number of various domain experts to collaborate in knowledge construction and verify the semantic meanings of the cross-domain concepts. Confusions and misinterpretations of concepts during knowledge creation are usually caused by having different perspectives and different business goals from different domain experts. In this paper, we propose a community-driven ontology-based application management (CD-OAM) framework that provides a collaborative environment with supporting features to enable collaborative knowledge creation. It can also reduce confusions and misinterpretations among domain stakeholders during knowledge construction process. We selected one of the multidisciplinary domains, which is Life Cycle Assessment (LCA) for our scenariobased knowledge construction. Constructing the LCA knowledge requires many concepts from various fields including environment protection, economic development, social development, etc. The output of this collaborative knowledge construction is called MLCA (multidisciplinary LCA) ontology. Based on our scenario-based experiment, it shows that CD-OAM framework can support the collaborative activities for MLCA knowledge construction and also reduce confusions and misinterpretations of crossdomain concepts that usually presents in general approach. key words: sematic web, ontology-based knowledge management, collaborative framework, multidisciplinary ontology development, life cycle assessment
This paper describes a collaborative approach to ontology development for data qualification for life cycle assessment by taking into consideration the Life Cycle Inventory (LCI) and Data Quality Indicator (DQI). The developed ontology is integrated with rule-based knowledge, to provide userdefined policies for LCI based on DQI. An ontology application management framework is developed to provide a collaborative environment for knowledge engineers and domain experts to define the knowledge explication and recommendation rules based on usage scenario. LCI data from agricultural domain is collected, and mapped to the knowledge base. To demonstrate the advantage of transformed rules, a scenario-based recommender system is built on top of the ontology, and carries out data quality measurement.
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.
When domain experts try to find a business solution, ambiguous terms arise within the context of discussion in a multidisciplinary research group. Different meanings and relationships with various concepts cause ambiguous semantics. This research aims to address complex business problems in a collaborative research group. This approach presents a collaborative framework based on network text analysis for detecting cross-disciplinary concepts in a multidisciplinary context. The framework recognizes ambiguous concepts (common terms presented in multiple domain knowledge), and these terms are visualized as a network. A case study of sustainable development demonstrates the identification of a set of cross-disciplinary concepts and their relationships across different domains. The main contributions are providing a framework to detect essential concepts that contain the cross-disciplinary concepts and recognize the understanding of multidisciplinary knowledge in the discussion context.
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