Abstract
Non-transparent shipping processes of transporting goods with special handling needs (special cargoes) have resulted in inefficiency in the airfreight industry. Special cargo ontology elicits, structures, and stores domain knowledge and represents the domain concepts and relationship between them in a machine-readable format. In this paper, we proposed an ontology population pipeline for the special cargo domain, and as part of the ontology population task, we investigated how to build an efficient information extraction model from low-resource domains based on available domain data for industry use cases. For this purpose, a model is designed for extracting and classifying instances of different relation types between each concept pair. The model is based on a relation representation learning approach built upon a Hierarchical Attention-based Multi-task architecture in the special cargo domain. The results of experiments show that the model could represent the complex semantic information of the domain, and tasks initialized with these representations achieve promising results.
Original language | English |
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Title of host publication | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings |
Publisher | IEEE |
ISBN (Electronic) | 9781728190488 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States Duration: 5 Dec 2021 → 7 Dec 2021 |
Publication series
Name | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings |
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Conference
Conference | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 |
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Country/Territory | United States |
City | Orlando |
Period | 5/12/21 → 7/12/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Natural Language Processing
- Relation Extraction
- Special Cargo Domain
- Transportation Ontology