Research on Construction and Automatic Expansion of Multi-source Lexical Semantic Knowledge Base

Abstract

With the development of research on improving the performance of deep learning models in combination with the rich knowledge resources in traditional knowledge bases, more and more research on building knowledge bases has become a hot topic. How to use the rich semantic information of existing knowledge bases such as HowNet and Tongyici-Cilin to build a more comprehensive and higher quality knowledge graph has become the focus of scholars’ research. In this work, we propose a way to integrate a variety of knowledge base information to build a new knowledge base, combined with deep learning techniques to expand the knowledge base. Successfully build a multi-source lexical semantic knowledge base through the steps of new ontology construction, data cleaning and fusion, and new knowledge expansion. Based on the establishment of the knowledge base, we use the graph database and JavaScript script to store and visualize the data separately. Through experiments, we obtained a lexical semantic knowledge base containing 153754 nodes, 1598356 triples and 137 relationships. It can provide accurate and convenient knowledge services, and can use a large number of semantic knowledge resources to support research on semantic retrieval, intelligent question answering system, semantic relationship extraction, semantic relevance calculation and ontology automatic construction [1].