In the dynamic landscape of modern education, the search for improved pedagogical methods, enriched learning experiences, and empowered educators remains a perpetual pursuit. In recent years, a remarkable technological innovation has asserted its dominance in education: Knowledge Graphs (KGs). These structured representations of knowledge are increasingly proving to be indispensable tools, fostering advancements driven by the growing recognition of their essential role in enriching personalised learning, curriculum design, concept mapping, and educational content recommendation systems. In this paper, a systematic literature review (SLR) has been conducted to comprehensively examine KG construction methodologies and their applications across five key domains in education. In each examined study, we highlight the specific KG functionalities, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations. This paper distinguishes itself by offering a broad overview of KGs in education, analyzing state-of-the-art methodologies, and identifying research gaps and limitations, paving the way for future advancements.

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird’s eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.

Purpose: This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs. Design/methodology/approach: This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets. Findings: Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics. Research limitations/implications: This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data. Practical implications: This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc. Originality/value: This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today’s competitive markets. A product with good affective design excites consumer emotional feelings to buy the product. Affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This review article presents a survey of commonly used machine learning approaches for affective design when two data streams, traditional survey data and modern big data, are used. A classification of machine learning technologies is first provided for traditional survey data. The limitations and advantages of machine learning technologies are discussed. Since big data related to affective design can be captured from social media, the prospects and challenges in using big data are discussed to enhance affective design, in which limited research has so far been attempted. This review article is useful for those who use machine learning technologies for affective design, and also provides guidelines for researchers who are interested in incorporating big data and machine learning technologies for affective design.

Peer-to-Peer (P2P) energy trading has gained much attention recently due to the advanced development of distributed energy resources. P2P enables prosumers to trade their surplus electricity and allows consumers to purchase affordable and locally produced renewable energy. Therefore, it is significant to develop solutions that are able to forecast energy consumption and generation toward better power management, thereby making renewable energy more accessible and empowering prosumers to make an informed decision on their energy management. In this paper, several models for forecasting short-term renewable energy consumption and generating are developed and discussed. Real-time energy datasets were collected from smart meters that were installed in residential premises in Western Australia. These datasets are collected from August 2018 to Apr 2019 at fine time resolution down to 5 s and comprise energy import from the grid, energy export to the grid, energy generation from installed rooftop PV, energy consumption in households, and outdoor temperature. Several models for forecasting short-term renewable energy consumption and generating are developed and discussed. The empirical results demonstrate the superiority of the optimised deep learning-based Long Term Short Memory (LSTM) model in forecasting both energy consumption and generation and outperforms the baseline model as well as the alternative classical and machine learning methods by a substantial margin.