A Construction of Knowledge Graph for Semiconductor Industry Chain Based on Lattice-LSTM and PCNN Models

A Construction of Knowledge Graph for Semiconductor Industry Chain Based on Lattice-LSTM and PCNN Models

This paper mainly focuses on building the knowledge graph of semiconductor industry chain. The main research contents include knowledge extraction, knowledge storage, and construction of knowledge graph in semiconductor field. The crawler technology and character recognition technology are used to obtain semiconductor industry chain information from the Internet, magazines, and institutions to establish the original data set. Then, Lattice Long Short-Term Memory (Lattice-LSTM) model is used to implement the entity extraction and recognition. The piecewise convolutional neural network (PCNN) model based on the sentence-level attention mechanism is used to extract relationships and obtain entity triples. The semiconductor dictionary library is constructed through the obtained structured data. The dictionary library and Chinese natural language toolkit HanLP are combined to annotate unstructured text data for knowledge extraction. Neo4j graph database is used to store the extracted data of semiconductor industry chain. Finally, Spring Boot and Vue technology are used to create a knowledge graph system.