Knowledge Representation Model Based on Case-Based Reasoning and the Domain Ontology: Application to the IT Consultation

Avdeenko, Tatiana; Makarova, Ekaterina

doi:10.1016/j.ifacol.2018.08.424

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…It will allow the teaching and administrative faculty of the educational institute to identify the trend of ongoing progress of the individual along with the identification for the need for further transformation and personalisation. However, these should be updated and maintained periodically for facilitating the process of the implementation personalised parameters [26]. It may be followed by the identification of the development for personal learning paths for each of the individuals, according to their level of knowledge, capacity, working memory, style of learning and other factors saving them from probable negative impacts of general educational settings.…”

Section: B Implementing Personalisation Parameters During Ongoing Sessionsmentioning

confidence: 99%

Analysis & Implementation of Personalisation Parameters in the Development of Computer-Based Adaptive Learning Environment

Al-Chalabi¹,

Hussein²

2020

SAR Journal

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The paper was aimed to identify the ways and to develop the model for the implementation personalised parameter for adaptive learning in the current E-learning environment. The study is qualitative in nature and relied on previous literature. The results highlighted that the personalised parameters which should be considered before the beginning of the course learning process include the level of learner’s knowledge, goals of the leaners, preferences of the language, style of learning, information seeking task, bandwidth, location, and previous level of knowledge. The study also pointed out learning needs, motivation levels, working memory capacity, intelligence, cognitive style, satisfaction, delight, and self-efficacy, the need for help, selfregulated learning, and ongoing interactions, waiting for feedback, ongoing progress, and navigation preference as the parameters for considering in ongoing sessions. The study formulated an implementation design comprising a combination of CBR and RBR strategies. The design is further categorised into standalone and coupling strategies, while the coupling strategy further includes a combination of embedded, co-processing and sequential processing strategies.

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Section: B Implementing Personalisation Parameters During Ongoing Sessionsmentioning

confidence: 99%

Analysis & Implementation of Personalisation Parameters in the Development of Computer-Based Adaptive Learning Environment

Al-Chalabi¹,

Hussein²

2020

SAR Journal

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“…After decades of research and application, the CBR has provided good support for knowledge reuse and reasoning in many fields. Avdeenko effectively combined CBR and ontology to add case information in the IT consulting field and established a connection with the domain ontology . Abdelwahed et al referred to empirical knowledge and designed a proprietary data structure and knowledge representation method. , A further efficient combination of the CBR and clustering algorithms in machine learning solved the problem of motion planning.…”

Section: Related Workmentioning

confidence: 99%

Feedstock Scheduling Optimization Based on Novel Extensible P-Graph Reasoning in Ethylene Production

Cao

Zhu

2020

Ind. Eng. Chem. Res.

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Ethylene production plants have great differences in terms of equipment, technology, and yields. Moreover, the production cost and carbon emission are closely related to the solution of feedstock scheduling. Therefore, how to choose a reasonable solution of feedstock scheduling is a great challenge for decision makers. Traditional modeling and selection strategies are involved with a large number of parameters and data. Fortunately, the superstructure base on process graph (P-Graph) is friendly to decision makers in both modeling and optimization. However, when the required solution is missing from the case base, the superstructure needs to be constructed and optimized again, which will consume a lot of resources and costs. To solve the above problems, this paper proposes a novel reasoning strategy based on extensible P-Graph. In the proposed methodology, P-Graph is regarded as the basic method of superstructure optimization. In the absence of available solutions, a reasoning strategy is integrated into extensible P-Graph to obtain new feedstock scheduling solutions. To verify the effectiveness of the proposed methodology, three cases of missing structural schemes, missing parameters and structural solutions, and missing target features are carried out. Simulation results indicate that the proposed methodology can provide a good solution selection strategy with decision makers, and the final required solution can be found. In addition, the burden of modeling and optimization can be greatly reduced.

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“…Ontology is a formal explicit description of concepts and their relations [35]. It attempts to represent entities, ideas, and events, along with their interdependent properties and relations, on the basis of a system of categories.…”

Section: Related Workmentioning

confidence: 99%

An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

Zhai

Ortega

Beltrán

et al. 2019

Sensors

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As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency.

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Knowledge Representation Model Based on Case-Based Reasoning and the Domain Ontology: Application to the IT Consultation

Cited by 8 publications

References 8 publications

Analysis & Implementation of Personalisation Parameters in the Development of Computer-Based Adaptive Learning Environment

Analysis & Implementation of Personalisation Parameters in the Development of Computer-Based Adaptive Learning Environment

Feedstock Scheduling Optimization Based on Novel Extensible P-Graph Reasoning in Ethylene Production

An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

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