eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems

Savonnet, Marinette; Leclercq, Éric; Naubourg, Pierre

doi:10.1109/jbhi.2015.2464353

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…eClims, see Savonnet et al (2016), is another example of an integration framework to deal with data and schema variability in Biomedical Information Systems (BIS). Since Biomedical research area has to deal with the constant integration of increasing number of databases and ontologies, they have created eClims to facilitate the integration of new data and extend data models at the same time assuring quality using Databases and Semantic Web theory.…”

Section: Background and Related Workmentioning

confidence: 99%

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

Koga¹,

Spichkova

Mantri

2019

Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering

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Cannabinoid research requires the cooperation of experts from various field biochemistry and chemistry to psychological and social sciences. The data that have to be managed and analysed are highly heterogeneous, especially because they are provided by a very diverse range of sources. A number of approaches focused on data collection and the corresponding analysis, restricting the scope to a sub-domain. Our goal is to elaborate a solution that would allow for automated management and analysis of heterogeneous data within the complete cannabinoids domain. The corresponding integration of diverse data sources would increase the quality and preciseness of the analysis. In this paper, we introduce the core ideas of the proposed framework as well as present the implemented prototype of a cannabinoids data platform. a a Preprint.

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Section: Background and Related Workmentioning

confidence: 99%

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

Koga¹,

Spichkova

Mantri

2019

Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering

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“…Based on conceptualization and various levels of generality, ontologies can be classified as application ontology, domain ontology, generic ontology, or representation ontology (Bimba et al, 2016). Application ontologies elicit the necessary characteristics needed to describe the relationship between concepts according to a specific task in a particular domain (Liu, Wang & Wu, 2010;Savonnet, Leclercq, & Naubourg, 2016). Alternatively, domain ontologies represent concepts that are only valid in a particular field.…”

Section: Knowledge Base Modeling Approachesmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

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“…However, application ontologies have method and task specific extensions (Jin Tan et al, 2005;Savonnet et al, 2015;Simperl, 2009;Van Heijst et al, 1997). An application ontology describes the relationship between concepts based on specific tasks (Liu et al, 2010).…”

Section: Application Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems

Cited by 10 publications

References 30 publications

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Towards knowledge modeling and manipulation technologies: A survey

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