Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Kamsu-Foguem, Bernard; Diallo, Gayo; Foguem, Clovis

doi:10.1016/j.engappai.2012.12.004

Cited by 55 publications

(31 citation statements)

References 72 publications

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“…The experts' knowledge generated in step 1 is translated into a formal specification expressed in the CG formalism. This formalism provides a variety of reasoning mechanisms [27], whose use is detailed in the next section, which will allow to [28]: (i) verify the correct expression of knowledge, (ii) ensure the overall consistency of expert knowledge, (iii) detect ''shortcomings'' requiring additional information, (iv) assist in communication and information exchange enabling actors to work in a distributed environment. Exploitation in maintenance: the problem solving method of a group of actors and the resulting analysis with solution to their problem are forms of knowledge that can be capitalized [15].…”

Section: Proposed Methodological Framework For Maintenance Managementmentioning

confidence: 99%

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Kamsu-Foguem

Noyes

2013

Computers in Industry

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Section: Proposed Methodological Framework For Maintenance Managementmentioning

confidence: 99%

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Kamsu-Foguem

Noyes

2013

Computers in Industry

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“…

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 2 1 ( 2 0 1 5 ) [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Case-based reasoning Evidence sources Medical taxonomy Oncology a b s t r a c t Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity.

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confidence: 99%

Telemedicine framework using case-based reasoning with evidences

Sene

Kamsu-Foguem

Rumeau

2015

Computer Methods and Programs in Biomedicine

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Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity. In addition to being a precious support in the procedure of medical decision-making, this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications. Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health. In this paper we enrich the case-based reasoning process by taking into account former evidence-based knowledge. We use the regular four steps approach and implement an additional (iii) step: (i) establish diagnosis, (ii) retrieve treatment, (iii) apply evidence, (iv) adaptation, (v) retain. Each step is performed using tools from knowledge engineering and information processing (natural language processing, ontology, indexation, algorithm, etc.). The case representation is done by the taxonomy component of a medical ontology model. The proposed approach is illustrated with an example from the oncology domain. Medical ontology allows a good and efficient modeling of the patient and his treatment. We are pointing up the role of evidences and specialist's opinions in effectiveness and safety of care.

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“…Especially, the graph-based approach has advantages over framebased models in expressing certain forms of modeling and in visual reasoning that facilitates an intuitive understanding [15].…”

Section: Knowledge Representation Modelsmentioning

confidence: 99%

“…conceptual graphs (CG) and semantic networks) and the frame-based approach (e.g. frame and description logics (DLs) [15].…”

Section: Knowledge Representation Modelsmentioning

confidence: 99%

“…Fourth, it supports the knowledge managers to confirm the knowledge hierarchies on the platform. It has advantages over text/frame-based models in expressing certain forms of knowledge framework modeling and in providing a visual reasoning [15]. Fifth, the logical rules can be used in the knowledge retrieval, predictions or classifications.It has the advantage of providing accurate information results with a certain level of probability of causation.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge Extractions, Visualizations, and Inference from the big Data in Healthcare and Medical

Kim¹

2013

Journal of Korean Institute of Intelligent Systems

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The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based onData Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extractslogical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refinesand manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using theDT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, inInference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

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Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Cited by 55 publications

References 72 publications

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Telemedicine framework using case-based reasoning with evidences

Knowledge Extractions, Visualizations, and Inference from the big Data in Healthcare and Medical

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