LILOG-DB: Database Support for Knowledge-Based Systems

Ludwig, Thomas; Walter, Bernd; Ley, Michael; Maier, Albert; Gehlen, Erich

doi:10.1007/978-3-642-74571-3_15

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…This system was able to answer questions relating to the Unix operating system. In addition, the LILOG project, a text-understanding system, was developed in the field of tourism information in a German city [2]. The systems developed in the UC and LILOG helped to advance the reasoning and computational linguistics.…”

Section: Introductionmentioning

confidence: 99%

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

Mutabazi

Tang

et al. 2021

Applied Sciences

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The advent of Question Answering Systems (QASs) has been envisaged as a promising solution and an efficient approach for retrieving significant information over the Internet. A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources. However, the medical domain receives less attention due to the shortage of medical datasets. Although Electronic Health Records (EHRs) are empowering the field of Medical Question-Answering (MQA) by providing medical information to answer user questions, the gap is still large in the medical domain, especially for textual-based sources. Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored. Furthermore, an in-depth analysis of deep learning approaches used in different MQA system tasks was provided. Finally, the different critical challenges posed by MQA systems were highlighted, and recommendations to effectively address them in forthcoming MQA systems were given out.

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Section: Introductionmentioning

confidence: 99%