Design And Construction Of Vertical-oriented Knowledge Graphs

Compared with traditional information management methods based on relational databases,knowledge graphs based on graph databases can easily and efficiently find the necessary association relationships between knowledge,and provide a basis for the efficient use of knowledge in artificial intelligence applications.Compared with the open domain knowledge graph,the vertical domain knowledge graph has the characteristics of relatively small total knowledge and relatively rich entity content.However,in the construction of the vertical domain knowledge graph,the traditional Chinese named entity recognition and Chinese based on the neural network model The entity relationship extraction algorithm has a single word vector representation and cannot adapt to the ambiguity characteristics of Chinese words in the Chinese information extraction process.This paper proposes a BiLSTM-CRF model and BiGRUAttention model,using the BERT pre-training language model to compare the existing The knowledge extraction technology in the construction of the vertical domain knowledge graph is improved.Using the BERT pre-training language model to fuse the BiLSTM-CRF model and the BiGRU-Attention model,the BERT-BiLSTM-CRF Chinese named entity recognition model and the BERT-BiGRU-Attention Chinese relationship extraction model are constructed.Among them,the BERT model jointly adjusts the contextual meaning of each layer,and uses the twoway Transformer as the encoder,which can dynamically generate and enrich the semantic vector of the characters,and solves the problem of the traditional word vector notation being unable to express the sentence features and the model accuracy is not high.The key step technology of knowledge extraction in the knowledge graph has been improved.The final experimental results show that the constructed Chinese named entity recognition model and Chinese relationship extraction model have F1 values of 95.12%and 82.79%,respectively,which are increased by 7.56%and 8.19%compared with other models.Apply the Chinese named entity recognition algorithm based on BERT-BiLSTM-CRF and the Chinese entity relationship extraction algorithm of BERT-BiGRU-Attention,aiming at the recommendation direction of the college entrance examination in the education field,according to the overall construction process of the vertical domain knowledge graph,through knowledge extraction,knowledge fusion,Process design of knowledge storage and knowledge visualization and complete the construction of knowledge graph in this field.Based on the constructed vertical domain knowledge graph,using Java development language,Spring Framwork framework and MySQL+Neo4j hybrid database for system development,designed and implemented an intelligent question-and-answer system and recommendation system for college entrance examination voluntary recommendation,and completed college entrance examination voluntary recommendation Construction of knowledge platform.The platform supports functions such as querying college information and enrollment information,volunteering question consultation,and voluntary recommendation,and has certain reference value for the construction and application of knowledge graphs in other vertical fields.