Ontology-driven Knowledge Management Research Articles

Ecosystem integration: the use of ontologies in integrating knowledge across manufacturing value networks

One of the greatest challenges in creating effective decision-making systems for connected enterprises is the management of cross-domain information. In manufacturing value networks where supply chains are increasingly intertwined, and closed-loop lifecycle management requires traversing several domains, ontologies are proving to be a reliable reference for cross-domain semantic interoperability. However, ontology development, implementation, and management are fragmented and difficult for new users of ontologies to grasp. This is a significant challenge in environments where ontologies are vital for managing effective data exchanges in complex industrial processes. The OntoCommons project has evolved an ontology ecosystem that aims to lower the entry barrier to using ontologies. Building on this ambition, we present a holistic approach to the integration and management of ontologies horizontally across manufacturing ecosystems, including the creation of reference documentation for manufacturing value networks and related standards, available tools for working with ontologies, and examples of vertical integration of knowledge from application level with domain-level and top-level ontology reference documentation. As a novel research direction, we propose a meta-level approach to ontology-driven knowledge management in manufacturing ecosystems. Based on evidence from recent breakthroughs, we present future and emerging research directions.

Knowledge Management Framework for Emerging Infectious Diseases Preparedness and Response: Design and Development of Public Health Document Ontology.

BackgroundThere are increasing concerns about our preparedness and timely coordinated response across the globe to cope with emerging infectious diseases (EIDs). This poses practical challenges that require exploiting novel knowledge management approaches effectively.ObjectiveThis work aims to develop an ontology-driven knowledge management framework that addresses the existing challenges in sharing and reusing public health knowledge.MethodsWe propose a systems engineering-inspired ontology-driven knowledge management approach. It decomposes public health knowledge into concepts and relations and organizes the elements of knowledge based on the teleological functions. Both knowledge and semantic rules are stored in an ontology and retrieved to answer queries regarding EID preparedness and response.ResultsA hybrid concept extraction was implemented in this work. The quality of the ontology was evaluated using the formal evaluation method Ontology Quality Evaluation Framework.ConclusionsOur approach is a potentially effective methodology for managing public health knowledge. Accuracy and comprehensiveness of the ontology can be improved as more knowledge is stored. In the future, a survey will be conducted to collect queries from public health practitioners. The reasoning capacity of the ontology will be evaluated using the queries and hypothetical outbreaks. We suggest the importance of developing a knowledge sharing standard like the Gene Ontology for the public health domain.

A knowledge management approach to capture organizational learning networks

Effective knowledge management practices in organizations are focused on knowledge creation and knowledge transfer activities. Thus, intelligence and competencies matters at the organizational workplace. For most knowledge intensive organizations is fundamental the continuous availability and development of domain expertise. This paper describes an ongoing research project to develop an organizational knowledge architecture that is being specified and developed to support collaboration tasks as well as design and model predictive data analysis and insights for organizational development. The primary goal of this research is to create a suitable architecture for use, initially, in intranet (corporate portal) collaborative procedures, but also scalable for later use in more generic forms of ontology-driven knowledge management systems. The designed architecture and functionalities aim to create coherent web data layers for intranet learning and predictive analysis, defining the vocabulary and semantics for knowledge sharing and reuse projects. Regarding intellectual capital definition, this research argues that effective knowledge management are based on the dynamic nature of the organizational knowledge, and predictive data analysis and insights identification can transform and add value to an organization. This paper presents a knowledge management and engineering perspective (ontology based) for the application of predictive analysis and insights at the organizational (corporate) workplace towards the development of the organizational learning network.

Collaborative ontology maintenance with concept maps and Semantic MediaWiki

The purpose of this study was to explore how a platform for collaborative ontology maintenance could enhance curriculum development in the domain of ICT-related vocational education. Although ontology engineering and maintenance are increasingly important in the era of Semantic Web, engaging the community in ontology-driven knowledge management is still challenging in many areas, including curriculum development. The result of our study is an empirically validated prototype of a collaborative ontology maintenance solution built with Semantic MediaWiki and Concept Maps, guided by the Methontology life cycle model. The method enabled the researchers also to evaluate external validity of the domain ontology for vocational ICT education.

Quality-of-Data Broker for Quality-of-Data-Aware Telemedicine Systems

Purpose: Telemedicine systems must provide clinical data of sufficient quality (according to medical standards) to support safe treatment guidance of outpatients. Quality of clinical data (QoD) typically varies due to unstable performance of ICT-components of these telemedicine systems. Therefore, telemedicine systems that support treatment guidance of outpatients should be QoD-aware and must be able to appropriately adapt treatment guidance to QoD variations. Only in this way, the effectiveness of treatment guidance and the safety of outpatients can be guaranteed.Methods: This paper follows a design science approach for the development of a functional architecture for QoD-aware telemedicine systems, with emphasis on one key component: the QoD Broker. Existing requirements elicitation methods were refined to deal with capturing QoD-specific requirements. Furthermore, an ontology-driven knowledge management method is proposed to enable the correct interpretation and manipulation of QoD within telemedicine systems. The functional architecture was validated using various methods, including prototype experiments and expert interviews.Result: One of the key components of the proposed functional architecture is the QoD Broker, which is a novel component that adds QoD-awareness to telemedicine systems. The QoD Broker obtains quality of service data from ICT components within a given telemedicine system and uses different computational models to compute QoD. This paper presents the QoD Broker architecture and the QoD management techniques implemented in the QoD Broker: (1) QoD dimensions, (2) QoD evaluation (i.e. computational models), (3) QoD stratification models and (4) technological recommendations.Discussion: The paper presents partial results of the validation that was performed in the context of the European MobiGuide project. The validation confirms that the proposed QoD Broker satisfies stakeholders' requirements and is considered useful to support stakeholders' goals. Both patients and medical domain experts found the role of the QoD Broker essential to guarantee the safety of patients when they are using telemedicine systems.

Ontology-driven knowledge management for cognitive networks

Cognitive networks are still in their infancy and are envisaged for efficient and autonomic management of increasing complexity of communication networks. A cognitive network (CN) is self-aware, self-configuring, self-optimising, self-healing and self-protecting. Therefore, it has knowledge about itself, its building blocks and their interconnection, and can share this knowledge and reason based on this. For developing a CN, two elements are required: knowledge representation and the cognition loop. In this paper, we present existing research work on cognitive networks that refers to ontological network representations for telecommunication network management. We provide a brief summary of artificial intelligent techniques that are appropriate for the development of a CN as well as the managerial implications of CNs. Architectural proposals, which are based on ontologies and comply with the requirements for a CN, are also presented.