Research On Construction Of Chinese Forestry Knowledge Graph Based On Deep Learning

With the rapid development of the Internet,data resources continue to accumulate,Chinese text information is increasing exponentially,and the value of data is still not fully explored,especially in forestry.Since entering modern times,forestry management tasks have become more and more complex,resulting in a large amount of redundant forestry knowledge,and there is an urgent need for a fast and efficient forestry information management method.In recent years,researchers have begun to explore the application of knowledge graph to the forestry field.Knowledge graph has powerful semantic processing and open interconnection capabilities,which help to quickly extract effective information from redundant data.Building a forestry knowledge graph can fuse fragmented forestry text data,and solve the problems of scattered,disordered,and weakly related knowledge in the current forestry network.Constructing a Chinese forestry knowledge graph is a new method to integrate forestry knowledge and manage forestry information resources.The main contents are as follows.(1)The dissertation conducted systematic research on the construction of Chinese forestry knowledge graph and analyzed the deficiencies of related work by investigating domestic and foreign knowledge graph construction technology,forestry status,and the status of subtask named entity recognition and entity relation extraction.In view of the confusion of forestry status and the construction of traditional knowledge graph relying on statistical machine learning and expert knowledge,a method for constructing Chinese forestry knowledge graph based on deep learning is proposed.(2)The method of optimizing the fusion of BERT and bidirectional RNN is used for forestry entity recognition and entity relation extraction tasks to improve the accuracy of forestry information extraction.The BERT model preprocessing text based on the whole word Mask can automatically extract the rich word-level and semantic features in the sequence,and solve the problem that the current word vectorization processing in information extraction cannot make full use of prior knowledge and contextual semantics.A variant of the bidirectional RNN(BiLSTM,BiGRU)performs bidirectional analysis and modeling of sentences,strengthens contextual semantic connection,and makes full use of text structure information,to improve the efficiency of forestry knowledge extraction.(3)In order to verify the validity and versatility of the model,comparative experiments are carried out on different datasets.On the common dataset,the F1-score of the named entity recognition BERT-BiLSTM-CRF model reached 96%,and the Fl-score of the entity relation extraction BERT-BiGRU-Dual Attention model also increased to 85%,with good performance;on the self-built forestry dataset,the recall and accuracy of the entity relation extraction experiment of BERT-BiGRU-Dual Attention can reach 75%and 80%respectively,and the accuracy of the named entity recognition experiment based on BERT-BiLSTM-CRF reached 90%.(4)Through the research on the construction of forestry knowledge graph,a bottom-up method of constructing forestry knowledge graph is proposed,and the forestry knowledge graph system is constructed by integrating the Django development framework and key algorithms such as forestry entity named recognition and forestry entity relation extraction.