scholarly journals Knowledge Representation and Validation in a Decision Support System: Introducing a Variability Modelling Technique

10.5772/16366 ◽  
2011 ◽  
Author(s):  
Abdelrahman Osman ◽  
Saravanan Muthaiyah ◽  
Chin Kuan ◽  
Somnuk Phon-Amnuaisuk
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Support System ◽  
Modelling Technique

Ontological Knowledge Representation and Inference Rules for MTM Decision Support System

2014 ◽  
Author(s):  
Rahul Renu ◽  
Gregory Mocko
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Support System ◽  
Time Estimation ◽  
Time Measurement ◽  
Inference Rules ◽  
Assembly Time ◽  
Time Estimates ◽  
Definition Of

The objective of the research presented is to develop and implement an ontological knowledge representation for Methods-Time Measurement assembly time estimation process. The knowledge representation is used to drive a decision support system that provides the user with intelligent MTM table suggestions based on assembly work instructions. Inference rules are used to map work instructions to MTM tables. An explicit definition of the assembly time estimation domain is required. The contribution of this research, in addition to the decision support system, is an extensible knowledge representation that models work instructions, MTM tables and mapping rules between the two which will enable the establishment of assembly time estimates. Further, the ontology provides an extensible knowledge representation framework for linking time studies and assembly processes.

scholarly journals Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Agnieszka Nowak-Brzezińska
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Support System ◽  
Inference Algorithm ◽  
Rule Based ◽  
New Knowledge ◽  
The Impact ◽  
Base Structure

Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results.

scholarly journals The HealthAgents ontology: knowledge representation in a distributed decision support system for brain tumours

2011 ◽  
Vol 26 (3) ◽  
pp. 303-328 ◽  
Author(s):  
Bo Hu ◽  
Madalina Croitoru ◽  
Roman Roset ◽  
David Dupplaw ◽  
Miguel Lurgi ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Brain Tumour ◽  
Support System ◽  
Brain Tumours ◽  
Data Access ◽  
Tumour Diagnosis ◽  
Brain Tumour Diagnosis ◽  
Distributed Decision

AbstractIn this paper we present our experience of representing the knowledge behind HealthAgents (HA), a distributed decision support system for brain tumour diagnosis. Our initial motivation came from the distributed nature of the information involved in the system and has been enriched by clinicians’ requirements and data access restrictions. We present in detail the steps we have taken towards building our ontology starting from knowledge acquisition to data access and reasoning. We motivate our representational choices and show our results using domain examples used by clinical partners in HA.

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