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

知识图谱作为一个巨大的知识网络图,其中包含着实体概念、关系等信息.基于深度学习的语义表示虽然泛化性强,但对于一些专有知识的敏感度不高,所以许多研究尝试将知识图谱与神经网络结合.目前大部分知识图谱语义表示的方法是围绕通用领域知识图谱展开的,没有针对学术领域的知识图谱语义表示方法的研究.本文以学术文献的全文本数据为研究对象,从基于学术知识图谱的语义表示方法切入研究,在构建学术知识图谱的基础上,对通用领域的研究方法(K-BERT)进行领域化改进(KEBERT),进一步使用实体知识增强文本的语义信息.通过开展下游任务的对比实验,在学术检索数据集上验证KEBERT、K-BERT和ERNIE的性能.实验采用检索任务中常用的NDCG评价指标对结果进行评价,实验结果表明改进后的KEBERT在检索任务上的效果优于其他模型.

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.