Knowledge management practice of medical cloud logistics industry: transportation resource semantic discovery based on ontology modelling. Issue 2 (29th September 2020)

• Record Type: Journal Article
• Authors: Zhou, Fuli; He, Yandong; Ma, Panpan; Mahto, Raj V.
• Is Part Of: Journal of intellectual capital. Volume 22:Issue 2(2021)
• Page Start: 360
• Page End: 383
• Publication Date: 2020-09-29
• Subjects: Cloud medical logistics -- Ontology modelling -- Transportation resource semantic discovery -- Rule reasoning -- Resource-task matching; Intellectual capital -- Periodicals; Human capital -- Periodicals; Knowledge management -- Periodicals; 658.4038
• DOI: 10.1108/JIC-03-2020-0072
• Languages: English
• ISSNs: 1469-1930

Abstract

Abstract : Purpose: The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation. Design/methodology/approach: To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules. Findings: An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources. Research limitations/implications: The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry. Originality/value: To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.