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- Blomqvist, Eva, et al.
(författare)
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Towards causal knowledge graphs - position paper
- 2020
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS. ; , s. 58-62, s. 58-62
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Konferensbidrag (refereegranskat)abstract
- In this position paper, we highlight that being able to analyse the cause-effect relationships for determining the causal status among a set of events is an essential requirement in many contexts and argue that cannot be overlooked when building systems targeting real-world use cases. This is especially true for medical contexts where the understanding of the cause(s) of a symptom, or observation, is of vital importance. However, most approaches purely based on Machine Learning (ML) do not explicitly represent and reason with causal relations, and may therefore mistake correlation for causation. In the paper, we therefore argue for an approach to extract causal relations from text, and represent them in the form of Knowledge Graphs (KG), to empower downstream ML applications, or AI systems in general, with the ability to distinguish correlation from causation and reason with causality in an explicit manner. So far, the bottlenecks in KG creation have been scalability and accuracy of automated methods, hence, we argue that two novel features are required from methods for addressing these challenges, i.e. (i) the use of Knowledge Patterns to guide the KG generation process towards a certain resulting knowledge structure, and (ii) the use of a semantic referee to automatically curate the extracted knowledge. We claim that this will be an important step forward for supporting interpretable AI systems, and integrating ML and knowledge representation approaches, such as KGs, which should also generalise well to other types of relations, apart from causality. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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| 2. |
- Jerdhaf, Oskar, et al.
(författare)
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Implant Terms: Focused Terminology Extraction with Swedish BERT - Preliminary Results
- 2020
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Konferensbidrag (refereegranskat)abstract
- Certain implants are imperative to detect be-fore MRI scans. However, implant terms, like‘pacemaker’ or ‘stent’, are sparse and difficultto identify in noisy and hastily written elec-tronic medical records (EMRs). In this pa-per, we explore how to discover implant termsin Swedish EMRs with an unsupervised ap-proach.To this purpose, we use BERT, astate-of-the-art deep learning algorithm, andfine-tune a model built on pre-trained SwedishBERT. We observe that BERT discovers asolid proportion of indicative implant terms.
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