Ontology Knowledge Mining Based Association Rules Ranking

Idoudi, Rihab; Ettabaa, Karim Saheb; Solaiman, Basel; Hamrouni, Kamel

doi:10.1016/j.procs.2016.08.147

Cited by 15 publications

(10 citation statements)

References 15 publications

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“…The ontology was used to determine the association between items of the association rules, allowing rules with no association to be filtered out. Idoudi et al 65 introduced an approach that applied an ontology to identify interesting association rules. They used a clustering algorithm to extract a hierarchy of groups of concepts from the ontology.…”

Section: Knowledge‐based and Data‐mining Processesmentioning

confidence: 99%

Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

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Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

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Section: Knowledge‐based and Data‐mining Processesmentioning

confidence: 99%

Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

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“…In [14], Idoudi et al propose a method that uses knowledge that is extracted from large databases through the generation of association rules, to enrich the existing ontologies in a knowledge base with new semantic relationships that are identified among concepts, with the objective of increasing the knowledge of the domain. The proposed method consist of the following six steps: representation of the ontological relationships in rule format; extraction of rules with the Apriori algorithm; mapping of the association rule items of the database to the concepts of an ontology; classification of the association rules into three categories (new, existing and unexpected); validation of the new relationships by an expert in the domain; and enrichment of the ontology with the newly validated relationships.…”

Section: A Related Workmentioning

confidence: 99%

Prediction of Press-Fit Quality via Data Mining Techniques and Artificial Intelligence

Guerrero¹,

Alonso-Lavernia

Marmolejo

2019

IEEE Access

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Determining the press-fit quality of pieces in advance is of the utmost importance because it enables the reduction of the time that is invested in the process and the prevention of material losses. High predictive accuracy is essential in a classification model; however, several studies have shown that the class category of a new instance may be insufficient information for decision making. To provide additional information to the user, this study presents a novel system that is based on a hybrid model, which, in addition, to using a classifier, extracts a set of class association rules that enable the determination of which patterns influence the new instance to belong to a class category. To select the classifier, the accuracy, recall and F-measure metrics were utilized. The rules were obtained with the Apriori algorithm to show that this knowledge is automatically represented in an ontological scheme with the objective of applying the Pellet reasoner.

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“…In order to overcome the disadvantage of the large volume of rules derived from the application of data mining association algorithms to big medical databases, in [23] an ontology based on measures of great interest that favors the establishment of association rules hierarchies was proposed. Thus, this ontology knowledge mining approach is used to rank the semantically interesting rules.…”

Section: Association Rule Mining Given the Variety Of Traditionalmentioning

confidence: 99%

“…Nevertheless, to the best of our knowledge, there are not works which used association rule mining and Bayesian networks to analyze the decrease in the number of autopsies performed in a hospital; therefore Scientific Programming 5 [12] Bayesian networks Classification [13] Logistic regression, NB Classification [14] Re-RX, J48graft Classification [15] NB, SVM Classification [16] NB, SVM, RF Classification [17] J48, RF, KNN, NB, SVM Classification [18] NB, SVM, logistic regression, RF Classification [19] NB, OTM, InterVA-4 Classification [21] Decision tree, Neural Networks Classification [22] Association rules Association [23] Apriori Association [24] Fuzzy association rules Mining and fuzzy logic Association [25] Formal Concept Analysis Association…”

Section: Association Rule Mining Given the Variety Of Traditionalmentioning

confidence: 99%

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

Delgado

Rodríguez-Mazahua

Guzmán³

et al. 2018

Scientific Programming

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This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.

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Ontology Knowledge Mining Based Association Rules Ranking

Cited by 15 publications

References 15 publications

Semantic data mining in the information age: A systematic review

Semantic data mining in the information age: A systematic review

Prediction of Press-Fit Quality via Data Mining Techniques and Artificial Intelligence

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

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