Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey

Khan, Nasrullah; Ma, Zongmin; Ullah, Aman; Polat, Kemal

doi:10.1016/j.eswa.2022.117737

Cited by 16 publications

(5 citation statements)

References 153 publications

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“…The most striking feature of the study is that it detects BP values for each period by epoching the ECG signal. Research on ECG, PPG, and BP properties is carried out extensively in the literature [ 31 , 33 , 78 ]. Among these signals, QRS and other components exhibited in a heartbeat in the ECG signal, which is the research subject of the study, are generally preferred as feature input [ 27 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

Kandaz

Uçar

Polat

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman’s feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.

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Section: Discussionmentioning

confidence: 99%

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

Kandaz

Uçar

Polat

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The constructed user-item KG assisted the author in mitigating the cold-start problem. Building a KG for a recommender system in the context of CE was also discussed in [36][37][38].…”

Section: Social Customer Advocacy Incorporating Kgsmentioning

confidence: 99%

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

Abu-Salih,

Alotaibi

2023

Technologies

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The rise of online social networks has revolutionized the way businesses and consumers interact, creating new opportunities for customer word-of-mouth (WoM) and brand advocacy. Understanding and managing customer advocacy in the online realm has become crucial for businesses aiming to cultivate a positive brand image and engage with their target audience effectively. In this study, we propose a framework that leverages the pre-trained XLNet- (bi-directional long-short term memory) BiLSTM- conditional random field (CRF) architecture to construct a Knowledge Graph (KG) for social customer advocacy in online customer engagement (CE). The XLNet-BiLSTM-CRF model combines the strengths of XLNet, a powerful language representation model, with BiLSTM-CRF, a sequence labeling model commonly used in natural language processing tasks. This architecture effectively captures contextual information and sequential dependencies in CE data. The XLNet-BiLSTM-CRF model is evaluated against several baseline architectures, including variations of BERT integrated with other models, to compare their performance in identifying brand advocates and capturing CE dynamics. Additionally, an ablation study is conducted to analyze the contributions of different components in the model. The evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate that the XLNet-BiLSTM-CRF model outperforms the baseline architectures, indicating its superior ability to accurately identify brand advocates and label customer advocacy entities. The findings highlight the significance of leveraging pre-trained contextual embeddings, sequential modeling, and sequence labeling techniques in constructing effective models for constructing a KG for customer advocacy in online engagement. The proposed framework contributes to the understanding and management of customer advocacy by facilitating meaningful customer-brand interactions and fostering brand loyalty.

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“…As a technical system, knowledge map is representative of progress in knowledge engineering in the age of big data [ 23 ]. In recent years, knowledge mapping technology has been widely used in various fields [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. It can show the development and structural relationship of scientific knowledge through graphics and can create the mathematical expression that reveals scientific knowledge and laws to graphical expression.…”

Section: Introductionmentioning

confidence: 99%

Hot Spots and Trends in the Relationship between Cancer and Obesity: A Systematic Review and Knowledge Graph Analysis

Gao

Yang

Xue

et al. 2023

Life

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Cancer is one of the most difficult medical problems in today’s world. There are many factors that induce cancer in humans, and obesity has become an important factor in inducing cancer. This study systematically and quantitatively describes the development trend, current situation and research hotspot of the relationship between cancer and obesity by using document statistics and knowledge graph visualization technology. Through the visualization technology analysis of knowledge graph in this study, the research hotspot and knowledge base source of the relationship between cancer and obesity in the last 20 years have been ascertained. Obesity-related factors, such as immunity, insulin, adiponectin, adipocytokines, nonalcoholic fatty liver and inflammatory reaction, may affect the occurrence of obesity and increase the risk of cancer. Obesity-related cancers include respiratory cancer, colorectal cancer, hepatocellular cancer, prostate cancer, gastric cancer, etc. Our research provides direction and basis for future research in this field, as well as technical and knowledge basis support for experts and researchers in related medical fields.

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Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey

Cited by 16 publications

References 153 publications

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

Hot Spots and Trends in the Relationship between Cancer and Obesity: A Systematic Review and Knowledge Graph Analysis

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