学科建设是高校发展的核心, 随着高校学科建设的不断深入与强化, 学科建设信息持续增加, 且以离散的文件组织形式难以对学科建设成果进行高效的管理, 不利于后续分析与评估工作的开展. 针对此问题, 对学科建设知识图谱的构建及相关应用进行了研究. 首先通过BERT-BiLSTM-CRF模型对学科建设文本进行事件抽取, 并使用爬虫进行相关知识的补充. 然后选择属性图模型存储知识, 完成学科建设知识图谱的初步构建. 基于构建好的知识图谱, 搭建了学科建设可视化系统, 并引入最小斯坦纳树算法实现智能问答应用. 最后, 通过对学科建设事件抽取与智能问答方法进行实验分析, 验证了本文所提出方法的有效性.
Discipline construction is the core of the development of colleges and universities. With the deepening and strengthening of discipline construction in colleges and universities, the information on discipline construction increases continuously. Nevertheless, the results of discipline construction can not be effectively managed in the manner of discrete document organization, which is not conducive to subsequent analysis and evaluation. To solve this problem, this study focuses on the construction and further application of discipline construction-oriented knowledge graphs. For this purpose, events are extracted from discipline construction texts by the BERT-BiLSTM-CRF model, and related knowledge is supplemented by the crawler. Then, the property graph model is selected to store knowledge, and a preliminary discipline construction-oriented knowledge graph is thereby built. Subsequently, this knowledge graph is availed to build a visualization system for discipline construction, and the minimum Steiner tree algorithm is adopted for the application of intelligent question answering. Finally, the validity of the proposed method is verified by experimental analysis of the methods of discipline construction-oriented event extraction and intelligent question answering.