A Deep Learning Knowledge Graph Approach to Drug Labelling

Sastre, Javier M.; Zaman, Faisal; Duggan, Nóirín; McDonagh, Caitlin; Walsh, Paul

doi:10.1109/bibm49941.2020.9313350

Cited by 2 publications

(2 citation statements)

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“…There are a number of articles that use the term knowledge graph, and construct it using causal relations. This technique has been used in the health (Rotmensch et al 2017;Bakal et al 2018;Yu 2020a), chatbots (Yu 2020b) and drug (Sastre et al 2020) domains. The papers discovered in this review found articles describing symptoms and the disease that they are associated with (Rotmensch et al 2017).…”

Section: Causal Information Modellingmentioning

confidence: 99%

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A survey of the extraction and applications of causal relations

Drury

Oliveira

Lopes³

2022

Nat. Lang. Eng.

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Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.

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Section: Causal Information Modellingmentioning

confidence: 99%

“…2018; Yu 2020a), chatbots (Yu 2020b) and drug (Sastre et al . 2020) domains. The papers discovered in this review found articles describing symptoms and the disease that they are associated with (Rotmensch et al .…”

Section: Applications Of Crementioning

confidence: 99%