Chinese Relation Extraction on Forestry Knowledge Graph Construction

作     者：Qi Yue Xiang Li Dan Li 

作者机构：College of Information and Computer EngineeringNortheast Forestry UniversityHarbin150040China 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2021年第37卷第6期

页      面：423-442页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0829[工学-林业工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by Heilongjiang Provincial Natural Science Foundation of China(Grant No.LH2019G001) Q.Yue would like to thank the foundation for financial support. 

主　　题：Forestry KG RE BiGRU dual attention FastText 

摘      要：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.