Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality