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Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks. As extracting rules manually from large knowledge graphs is labour-intensive and often infeasible, automated rule learning has recently attracted significant interest, and a number of approaches to rule learning for knowledge graphs have been proposed. This survey aims to provide a review of approaches and a classification of state-of-the-art systems for learning first-order logic rules over knowledge graphs. A comparative analysis of various approaches to rule learning is conducted based on rule language biases, underlying methods, and evaluation metrics. The approaches we consider include inductive logic programming (ILP)-based, statistical path generalisation, and neuro-symbolic methods. Moreover, we highlight important and promising application scenarios of rule learning, such as rule-based knowledge graph completion, fact checking, and applications in other research areas.

BackgroundThe internet enables consumers to evaluate products before purchase based on feedback submitted by like-minded individuals. Displaying reviews allows customers to assess comparable experiences and encourages trust, increased sales, and brand positivity. Customers use reviews to inform decision making, whereas organizations use reviews to predict future sales. Prior studies have focused on manufactured products, with little attention being paid to health care services. In particular, whether patients prefer to use websites to discuss doctors’ reputation has so far remained unanswered. ObjectiveThis study aims to investigate how patient propensity to post treatment experiences changes based on doctors’ online reputation (medical quality and service attitude) in delivering outpatient care services. Further, this study examines the moderating effects of hospitals’ (organizational) online reputation and disease severity. MethodsFractional logistic regression was conducted on data collected from 7183 active doctors in a Chinese online health community to obtain empirical results. ResultsOur findings show that patients prefer to share treatment experiences for doctors who have a higher medical quality and service attitude (βservice attitude=.233; P

BackgroundThe coronavirus disease (COVID-19) epidemic poses an enormous challenge to the global health system, and governments have taken active preventive and control measures. The health informatics community in China has actively taken action to leverage health information technologies for epidemic monitoring, detection, early warning, prevention and control, and other tasks. ObjectiveThe aim of this study was to develop a technical framework to respond to the COVID-19 epidemic from a health informatics perspective. MethodsIn this study, we collected health information technology–related information to understand the actions taken by the health informatics community in China during the COVID-19 outbreak and developed a health information technology framework for epidemic response based on health information technology–related measures and methods. ResultsBased on the framework, we review specific health information technology practices for managing the outbreak in China, describe the highlights of their application in detail, and discuss critical issues to consider when using health information technology. Technologies employed include mobile and web-based services such as Internet hospitals and Wechat, big data analyses (including digital contact tracing through QR codes or epidemic prediction), cloud computing, Internet of things, Artificial Intelligence (including the use of drones, robots, and intelligent diagnoses), 5G telemedicine, and clinical information systems to facilitate clinical management for COVID-19. ConclusionsPractical experience in China shows that health information technologies play a pivotal role in responding to the COVID-19 epidemic.

BackgroundWeb-based crowdsourcing promotes the goals achieved effectively by gaining solutions from public groups via the internet, and it has gained extensive attention in both business and academia. As a new mode of sourcing, crowdsourcing has been proven to improve efficiency, quality, and diversity of tasks. However, little attention has been given to crowdsourcing in the health sector. ObjectiveCrowdsourced health care information websites enable patients to post their questions in the question pool, which is accessible to all doctors, and the patients wait for doctors to respond to their questions. Since the sustainable development of crowdsourced health care information websites depends on the participation of the doctors, we aimed to investigate the factors influencing doctors’ participation in providing health care information in these websites from the perspective of the elaboration-likelihood model. MethodsWe collected 1524 questions with complete patient-doctor interaction processes from an online health community in China to test all the hypotheses. We divided the doctors into 2 groups based on the sequence of the answers: (1) doctor who answered the patient’s question first and (2) the doctors who answered that question after the doctor who answered first. All analyses were conducted using the ordinary least squares method. ResultsFirst, the ability of the doctor who first answered the health-related question was found to positively influence the participation of the following doctors who answered after the first doctor responded to the question (βoffline1=.177, P

BackgroundGift giving from patients to physicians, which is prohibited in traditional clinical settings in China, has been found to occur in online health communities. However, there is debate on the validity of online gifts since physicians gain an economic benefit. Moreover, the potential impact of these gifts, particularly with respect to the financial value of the gift, on the online consultation service quality remains unexplored. ObjectiveThe aim of this study was to explore the impact of gift price on the quality of physicians’ online consultation service. Insight into this impact is expected to help resolve existing debate on the appropriateness of the gift-giving practice in online consultations. MethodsA dataset of 141 physicians and 4249 physician-patient interactions was collected from the Good Physician Online website, which is the largest online consultation platform in China. Based on social exchange theory, we investigated how gift price affects the quality of physicians’ online consultation service and how this impact changes according to the physician’s service price and number of all gifts received. Manual annotation was used to identify the information support paragraphs and emotional support paragraphs in the answers of physicians. The quality of the information support paragraphs, rather than the complete answer, was used to test the robustness of our model. ResultsGift price had a positive impact on the quality of physicians’ online consultation service (β=4.941, P

BackgroundOnline healthcare communities are changing the ways of physician-patient communication and how patients choose outpatient care physicians. Although a majority of empirical work has examined the role of online reviews in consumer decisions, less research has been done in health care, and endogeneity of online reviews has not been fully considered. Moreover, the important factor of physician online services has been neglected in patient decisions. ObjectiveIn this paper, we addressed the endogeneity of online reviews and examined the impact of online reviews and services on outpatient visits based on theories of reviews and channel effects. MethodsWe used a difference-in-difference approach to account for physician- and website-specific effects by collecting information from 474 physician homepages on two online health care communities. ResultsWe found that the number of reviews was more effective in influencing patient decisions compared with the overall review rating. An improvement in reviews leads to a relative increase in physician outpatient visits on that website. There are channel effects in health care: online services complement offline services (outpatient care appointments). Results further indicate that online services moderate the relationship between online reviews and physician outpatient visits. ConclusionsThis study investigated the effect of reviews and channel effects in health care by conducting a difference-in-difference analysis on two online health care communities. Our findings provide basic research on online health care communities.