TY  - INPR
ID  - SisLab4175
UR  - https://icatse.org/icisa2020/
A1  - Nguyen, Ngoc Vu
A1  - Tran, Mai Vu
A1  - Nguyen, Hai Chau
A1  - Ha, Quang Thuy
Y1  - 2020/12/16/
N2  - Building domain ontology is a challenging problem, and there are many different approaches for domain ontology construction. However, most of these approaches are still mainly using manual methods [1]. Ontology enrichment is a fairly standard approach in domain ontology construction, in which semi-automated methods and automated methods of ontology learning from a derived ontology. Relation extraction is one of the ways for ontology enrichment. Relation extraction techniques include law-based techniques, machine learning-based techniques with three typical methods: supervised learning, semi-supervised learning, and unsupervised learning. This paper proposes a word + character embedding-based relation extraction frame for the Vietnamese domain ontology of natural resources and environment. The model's effect was demonstrated by experiments in the domain of natural resources and the envi-ronment and achieving promising results.
TI  - A Word + Character Embedding based Relation Extraction Frame for Domain Ontology of Natural Resources and Environment
M2  - Virtual Conference
AV  - public
T2  - 11th International Conference on Information Science and Applications 2020 (ICISA2020)
ER  -