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https://doi.org/10.21256/zhaw-26847| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Ontology-aware biomedical relation extraction |
| Authors: | Aghaebrahimian, Ahmad Anisimova, Maria Gil, Manuel |
| et. al: | No |
| DOI: | 10.1007/978-3-031-16270-1_14 10.21256/zhaw-26847 |
| Proceedings: | Text, Speech, and Dialogue |
| Editors of the parent work: | Sojka, Petr Horák, Aleš Kopeček, Ivan Pala, Karel |
| Page(s): | 160 |
| Pages to: | 171 |
| Conference details: | 25th International Conference on Text, Speech and Dialogue (TSD), Brno, Czech Republic, 6-9 September 2022 |
| Issue Date: | 2022 |
| Series: | Lecture Notes in Computer Science |
| Series volume: | 13502 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-031-16269-5 978-3-031-16270-1 |
| Language: | English |
| Subjects: | Biomedical relation extraction; Graph embedding; Deep neural network; Ontology; UMLS |
| Subject (DDC): | 410.285: Computational linguistics |
| Abstract: | Automatically extracting relationships from biomedical texts among multiple sorts of entities is an essential task in biomedical natural language processing with numerous applications, such as drug development or repurposing, precision medicine, and other biomedical tasks requiring knowledge discovery. Current Relation Extraction systems mostly use one set of features, either as text, or more recently, as graph structures. The state-of-the-art systems often use resource-intensive hence slow algorithms and largely work for a particular type of relationship. However, a simple yet agile system that learns from different sets of features has the advantage of adaptability over different relationship types without an extra burden required for system re-design. We model RE as a classification task and propose a new multi-channel deep neural network designed to process textual and graph structures in separate input channels. We extend a Recurrent Neural Network with a Convolutional Neural Network to process three sets of features, namely, tokens, types, and graphs. We demonstrate that entity type and ontology graph structure provide better representations than simple token-based representations for Relation Extraction. We also experiment with various sources of knowledge, including data resources in the Unified Medical Language System to test our hypothesis. Extensive experiments on four well-studied biomedical benchmarks with different relationship types show that our system outperforms earlier ones. Thus, our system has state-of-the-art performance and allows processing millions of full-text scientific articles in a few days on one typical machine. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26847 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Restricted until: | 2023-09-16 |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | Computational Literature-based Discovery Methods |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Aghaebrahimian-etal_Ontology-aware-biomedical-relation-extraction.pdf | Accepted Version | 150.61 kB | Adobe PDF |  View/Open |
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Aghaebrahimian, A., Anisimova, M., & Gil, M. (2022). Ontology-aware biomedical relation extraction [Conference paper]. In P. Sojka, A. Horák, I. Kopeček, & K. Pala (Eds.), Text, Speech, and Dialogue (pp. 160–171). Springer. https://doi.org/10.1007/978-3-031-16270-1_14
Aghaebrahimian, A., Anisimova, M. and Gil, M. (2022) ‘Ontology-aware biomedical relation extraction’, in P. Sojka et al. (eds) Text, Speech, and Dialogue. Cham: Springer, pp. 160–171. Available at: https://doi.org/10.1007/978-3-031-16270-1_14.
A. Aghaebrahimian, M. Anisimova, and M. Gil, “Ontology-aware biomedical relation extraction,” in Text, Speech, and Dialogue, 2022, pp. 160–171. doi: 10.1007/978-3-031-16270-1_14.
AGHAEBRAHIMIAN, Ahmad, Maria ANISIMOVA und Manuel GIL, 2022. Ontology-aware biomedical relation extraction. In: Petr SOJKA, Aleš HORÁK, Ivan KOPEČEK und Karel PALA (Hrsg.), Text, Speech, and Dialogue. Conference paper. Cham: Springer. 2022. S. 160–171. ISBN 978-3-031-16269-5
Aghaebrahimian, Ahmad, Maria Anisimova, and Manuel Gil. 2022. “Ontology-Aware Biomedical Relation Extraction.” Conference paper. In Text, Speech, and Dialogue, edited by Petr Sojka, Aleš Horák, Ivan Kopeček, and Karel Pala, 160–71. Cham: Springer. https://doi.org/10.1007/978-3-031-16270-1_14.
Aghaebrahimian, Ahmad, et al. “Ontology-Aware Biomedical Relation Extraction.” Text, Speech, and Dialogue, edited by Petr Sojka et al., Springer, 2022, pp. 160–71, https://doi.org/10.1007/978-3-031-16270-1_14.
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