湖南大学学报（自然科学版）2024，Vol.51Issue(6)：108-118,11.DOI:10.16339/j.cnki.hdxbzkb.2024271
基于学术知识图谱的增强语义表示与检索
Enhanced Semantic Representation and Retrieval Based on Academic Knowledge Graph
摘要
Abstract
As a huge knowledge network diagram,the knowledge graph contains entity concepts,relationships,and other information.Although the semantic representation based on deep learning has strong generalization,it is not sensitive to some proprietary knowledge,so many researchers try to combine knowledge graphs with neural network.At present,most of the methods of semantic representation of knowledge graphs are based on general domain knowledge graphs,and there is no research on the semantic representation of knowledge graphs in the academic field.In this paper,the full-text data of academic literature is taken as the research object,and the semantic representation method based on an academic knowledge graphs is studied.On the basis of constructing academic knowledge graph,the research method of the general field(K-BERT)is improved(KEBERT),and entity knowledge is further used to enhance the semantic information of the text.By conducting comparative experiments on downstream tasks,the performance of KEBERT,K-BERT,and ERNIE is verified on academic retrieval datasets.The experiment uses the NDCG evaluation index commonly used in the retrieval task to evaluate the results.The experimental results show that the improved KEBERT is superior to other models in the retrieval task.关键词
知识图谱/语义表示/增强语义/学术检索Key words
knowledge graph/semantic representation/enhanced semantics/academic retrieval分类
社会科学引用本文复制引用
沈思,严大钰,卞嘉欣,何宏旭..基于学术知识图谱的增强语义表示与检索[J].湖南大学学报（自然科学版）,2024,51(6):108-118,11.基金项目
国家自然科学基金资助项目(71974094),National Naturel Science Foundation of China(71974094) （71974094）
