4,814 research outputs found

    Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

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    Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics. We release our code to encourage further research.Comment: Accepted as Main Conference Long paper at COLING 202

    SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

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    In this era of knowledge economy in which knowledge have become the most precious resource, surveys have shown that e-Learning has been on the increasing trend in various organizations including, among others, education and corporate. The use of e-Learning is not only aim to acquire knowledge but also to maintain competitiveness and advantages for individuals or organizations. However, the early promise of e-Learning has yet to be fully realized, as it has been no more than a handout being published online, coupled with simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by Web 2.0 technology still hardly overcome common problem such as information overload and poor content aggregation in a highly increasing number of learning objects in an e-Learning Management System (LMS) environment. The aim of this research study is to exploit the Semantic Web (SW) and Knowledge Management (KM) technology; the two emerging and promising technology to enhance the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is the backbone of SW and KM is introduced for managing knowledge especially from learning object and developing automated question answering system (Aquas) with expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to develop the Ontology in this research work. The potential of SW and KM technology is identified in this research finding which will benefit e-Learning developer to develop e-Learning system especially with social constructivist pedagogical approach from the point of view of KM framework and SW environment. The (semi-) automatic ontological knowledge base construction system (SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically whilst the Aquas with expert locator has facilitated knowledge retrieval that encourages knowledge sharing in e-Learning environment. The experiment conducted has shown that the SAOKBCS can extract concept that is the main component of Ontology from text learning object with precision of 86.67%, thus saving the expert time and effort to build Ontology manually. Additionally the experiment on Aquas has shown that more than 80% of users are satisfied with answers provided by the system. The expert locator framework can also improve the performance of Aquas in the future usage. Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS), semi-automatic ontological knowledge base construction system (SAOKBCS), automated question answering system (Aquas), Ontology, expert locator

    In Search of an Adequate Yet Affordable Tutor in Online Learning Networks

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    Sloep, P., van Rosmalen, P., Kester, L., Brouns, F. M. R., & Koper, E. J. R. (2006). In search of an adequate yet affordable tutor in online learning networks. In search of an adequate yet affordable tutor in online learning networks. Presentation at the 6th IEEE International Conference on Advanced Learning Technologies. July, 5-7, 2006, Kerkrade, The Netherlands

    An E-Learning Semantic Grid for Life science Education

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    There are a lot of life science databases and services on the Internet nowadays, especially in life science e-science. In this paper, we will present an E-Learning Semantic Grid that integrates these resources provided by both teachers and scientists for life science education. It uses domain ontologies to integrate these heterogeneous life science database and service resources, and supports ontology-based e-learning data-sharing and service-coordination for life science teachers and students in an e-learning virtual organization. Our system provides life science students with semantically superior experience in learning activities, and also extends the function of life science e-science. It has a promising future in the domain of life science education

    Peripheral Innate Immune Activation Correlates With Disease Severity in GRN Haploinsufficiency.

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    Objective: To investigate associations between peripheral innate immune activation and frontotemporal lobar degeneration (FTLD) in progranulin gene (GRN) haploinsufficiency. Methods: In this cross-sectional study, ELISA was used to measure six markers of innate immunity (sCD163, CCL18, LBP, sCD14, IL-18, and CRP) in plasma from 30 GRN mutation carriers (17 asymptomatic, 13 symptomatic) and 29 controls. Voxel based morphometry was used to model associations between marker levels and brain atrophy in mutation carriers relative to controls. Linear regression was used to model relationships between plasma marker levels with mean frontal white matter integrity [fractional anisotropy (FA)] and the FTLD modified Clinical Dementia Rating Scale sum of boxes score (FTLD-CDR SB). Results: Plasma sCD163 was higher in symptomatic GRN carriers [mean 321 ng/ml (SD 125)] compared to controls [mean 248 ng/ml (SD 58); p < 0.05]. Plasma CCL18 was higher in symptomatic GRN carriers [mean 56.9 pg/ml (SD 19)] compared to controls [mean 40.5 pg/ml (SD 14); p < 0.05]. Elevation of plasma LBP was associated with white matter atrophy in the right frontal pole and left inferior frontal gyrus (p FWE corrected <0.05) in all mutation carriers relative to controls. Plasma LBP levels inversely correlated with bilateral frontal white matter FA (R2 = 0.59, p = 0.009) in mutation carriers. Elevation in plasma was positively correlated with CDR-FTLD SB (b = 2.27 CDR units/μg LBP/ml plasma, R2 = 0.76, p = 0.003) in symptomatic carriers. Conclusion: FTLD-GRN is associated with elevations in peripheral biomarkers of macrophage-mediated innate immunity, including sCD163 and CCL18. Clinical disease severity and white matter integrity are correlated with blood LBP, suggesting a role for peripheral immune activation in FTLD-GRN
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