Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

Vrablecová, Petra; Šimko, Marián

doi:10.1109/tlt.2016.2546255

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…Supervised learning is identified to be the more predominant type of machine learning. This could be at- Text [18], [24], [84], [19], [16], [44], [3], [13], [47], [47], [74], [55], [8], [7], [29], [43], [5], [28], [6], [53], [49] Images [58], [83], [37], [31], [89], [86], [15], [76], [21], [32], [81] 11 27.5…”

Section: Discussionmentioning

confidence: 99%

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Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Chang,

Sangodiah

2023

mendel

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Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web dependson the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. SupportVector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource; and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research.

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Section: Discussionmentioning

confidence: 99%

“…Quantity % Semi-automated [58], [50], [83], [18], [84], [31], [89], [86], [15], [16], [42], [44], [64], [47], [82], [74], [55], [21], [8], [7], [43], [28], [53] 23 57…”

Section: Degree Of Automation Studiesunclassified

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Chang,

Sangodiah

2023

mendel

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“…The trained model was built of out-of sample validation. Petra Vrablecova et al [5] studied about the semantic annotation of the educational data that built hierarchical domain relationship between the attributes. Dewan Md.…”

Section: Prior Workmentioning

confidence: 99%

“…One of the greatest difficulties that higher foundations confront today is the way to enhance the nature of administrative choices. The administrative basic leadership process turns out to be more mind boggling as the unpredictability of instructive elements increment [5]. The rest of the paper is organized as follows: Section II presents prior work; Section III presents proposed work; Section IV presents the experimental analysis and concludes in Section V…”

Section: Introductionmentioning

confidence: 99%

Predicting Instructors Performance in Higher Education Systems

Kalaiselvi¹,

Sowmiya²

2018

EAI Endorsed Transactions on Energy Web

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In recent years, knowledge mining has become one of the effective tools for data analysis and information management systems. Educational sector is the recent research endeavors that make use of data mining algorithms. Prior works carried in data mining algorithms like J48 Decision Tree, Multilayer Perception, Naïve Bayes, and Sequential Minimal Optimization impose issues like the curse of dimensionality, cardinality and imbalance attributes. In this paper, we have proposed FA-Paired t-test method which is a novel knowledge discovery process to predict the performance of the instructors. The aim of the study is to find the factors that associated for the prediction of teaching quality. Thus, the contribution of the factor analysis method helps to find the relevant attributes from a set of attributes. Then, the selected attributes are fed as input to paired t-test model which find the associations between those linked attributes. The selected attributes are experimenting using SPSS modeler. Many attributes test for the performance evaluation. It is strongly found that content arrangement, delivery of speech, effective class hours and completion of the course helps to predict the quality of the teaching. In addition to, the proposed model is compared to prior two classifiers, named, J48DT and Naïve Bayes which shows our proposed method works better than other two classifiers in term of Attribute reduction and evaluation process.

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Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

138

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

Cited by 13 publications

References 33 publications

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Predicting Instructors Performance in Higher Education Systems

Text mining in education

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