A distributed joint extraction framework for sedimentological entities and relations with federated learning. (1st March 2023)
- Record Type:
- Journal Article
- Title:
- A distributed joint extraction framework for sedimentological entities and relations with federated learning. (1st March 2023)
- Main Title:
- A distributed joint extraction framework for sedimentological entities and relations with federated learning
- Authors:
- Wang, Tianheng
Zheng, Ling
Lv, Hairong
Zhou, Chenghu
Shen, Yunheng
Qiu, Qinjun
Li, Yan
Li, Pufan
Wang, Guorui - Abstract:
- Abstract: Sedimentological knowledge graphs can be used to identify natural resources in earth layers, which may help geologists analyze the distribution of oil crude in earth, and therefore locating the oilfield that is unknown. The building of such knowledge graphs mainly counts on the methods of joint extraction for pairwise entities and the corresponding relations on large-scale data. However, the whole sedimentological data is fairly owned by the different parties with the possibly inconsistent format. Centralized processing on sedimentological data as a whole will be either securely or structurally impractical. Therefore, this paper proposes a framework of distributed joint extraction in order to harvest knowledge triplets on distributed sedimentological corpus that are from many disparate sources without data transmission. The experimental studies demonstrate our methods not only approach the previous state-of-the-art but also protect the data privacy and security for data holders. Highlights: A corpus is built upon sedimentological literature abstracts on our own. Manually annotate training and testing texts. Distributed deep learning framework is proposed for the joint extraction task. Federated learning is used to implement distributed deep learning framework. Federated learning with two novel parameter-sharing strategies.
- Is Part Of:
- Expert systems with applications. Volume 213:Part C(2023)
- Journal:
- Expert systems with applications
- Issue:
- Volume 213:Part C(2023)
- Issue Display:
- Volume 213, Issue 3 (2023)
- Year:
- 2023
- Volume:
- 213
- Issue:
- 3
- Issue Sort Value:
- 2023-0213-0003-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-03-01
- Subjects:
- Distributed joint extraction -- Federated learning -- Sedimentological corpus -- Data security
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2022.119216 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24578.xml