Open Access

ARTICLE

Chinese Relation Extraction on Forestry Knowledge Graph Construction

Qi Yue, Xiang Li, Dan Li^*

College of Information and Computer Engineering, Northeast Forestry University, Harbin, 150040, China

* Corresponding Author: Dan Li. Email:

Computer Systems Science and Engineering 2021, 37(3), 423-442. https://doi.org/10.32604/csse.2021.014448

Received 17 September 2020; Accepted 20 October 2020; Issue published 08 March 2021

Abstract

Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance. Based on the above observations, we proposed an end-to-end relation extraction method that used Bi-directional Gated Recurrent Unit (BiGRU) neural network and dual attention mechanism in forestry KG construction. The dual attention includes sentence-level and word-level, capturing relational semantic words and direction words. To enhance the performance, we used the pre-training model FastText to provide word vectors, and dynamically adjusted the word vectors according to the context. We used forestry entities and relationships to build forestry KG and used Neo4j to store forestry KG. Our method can achieve better results than previous public models in the SemEval-2010 Task 8 dataset. By training the model on forestry dataset, results showed that the accuracy and precision of FastText-BiGRU-Dual Attention exceeded 0.8, which outperformed the comparison methods, thus the experiment confirmed the validity and accuracy of our model. In the future, we plan to apply forestry KG to question and answer system and achieve a recommendations system for forestry knowledge.

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Citations