一种可快速迁移的领域知识图谱构建方法

Fast and Transmissible Domain Knowledge Graph Construction Method

领域知识图谱能清晰可视化地表示领域实体关系,高效准确地获取领域知识。构建领域知识图谱有助于推进相关领域的信息化发展,但构建领域知识图谱需要领域专家耗费大量的人力与时间成本,且很难迁移到其他领域中。为减少人工耗费,提升知识图谱构建方法的普适性,文中提出一种不依赖大量人工本体构建与数据标记的领域知识图谱通用构建方法;通过领域词典构建、数据获取与清洗、实体维护与链接、图谱更新与可视化4个步骤构建相关领域知识图谱。文中以网络安全领域为例构建知识图谱,详细介绍构建流程。同时,为确保图谱信息的领域相关性,文中提出一种基于BERT(Bidirectional Encoder Representations from Transformers)迁移模型与注意力机制的融合模型,该模型在文本分类中得到87.14%的F1值和93.51%的准确率。

Domain knowledge graph can clearly and visually represent domain entity relations,acquire knowledge efficiently and accurately.The construction of domain knowledge graph is helpful to promote the development of information technology in rela-ted fields,but the construction of domain knowledge graph requires huge manpower and time costs of experts,and it is difficult to migrate to other fields.In order to reduce the manpower cost and improve the versatility of knowledge graph construction me-thod,this paper proposes a general construction method of domain knowledge graph,which does not rely on a large of artificial ontology construction and data markup.The domain knowledge graph is constructed through four steps:domain dictionary construction,data acquisition and cleaning,entity linking and maintenance,and graph updating and visualization.This paper takes the domain of network security as an example to construct the knowledge graph and details the build process.At the same time,in order to improve the domain correlation of entities in the knowledge graph,a fusion model based on BERT(Bidirectional Encoder Representations from Transformers)and attention mechanism model is proposed in this paper.The F-score of this model in text classification is 87.14%,and the accuracy is 93.51%.