摘要
提出一种宠物知识图谱的构建框架。通过自顶向下的方式设计并构建了schema(概念)层,从半结构化和非结构化数据中进行知识抽取构建了数据层。在对非结构化数据的实体抽取方面,提出了一种条件随机场(CRF)与宠物症状词典相结合的症状命名实体识别方法。该方法利用症状词典对文本进行识别,获取语义类别信息,CRF结合语义信息实现对症状实体的识别抽取。实验结果表明了该方法的有效性。在知识表示方面,选用Orient DB数据库支持的属性图模型来表示。知识图谱采用Orient DB图数据库来完成知识的存储,并实例展示了构建的宠物知识图谱。
This paper proposed a construction framework of pet knowledge graph.It designed and constructed the schema layer in a top-down manner and constructed the data layer by extracting knowledge from semi-structured and unstructured data.For entity extraction of unstructured data,this paper proposed a symptom-named entity recognition method which combined conditional random field(CRF)and pet symptom dictionary.The method used symptom dictionary to identify the text and obtained the semantic category information,and then combined CRF and the semantic information to identify symptom-named entities.The experimental results show the effectiveness of the method.It selected the attribute graph model supported by the OrientDB database for knowledge representation.The knowledge graph used the OrientDB graph database for knowledge storage.In addition,it showed the constructed pet knowledge graph.
作者
袁琦
刘渊
谢振平
陆菁
Yuan Qi;Liu Yuan;Xie Zhenping;Lu Jing(School of Digital Media,Jiangnan University,Wuxi Jiangsu 214122,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第1期178-182,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61672264)
国家科技支撑计划资助项目(2015BAH54F01)
江苏省研究生科研与实践创新计划项目(SJCX17_0505).
关键词
宠物知识图谱
症状术语词典
宠物症状命名实体识别
条件随机场
图数据库
pet knowledge graph
symptoms dictionary
pet symptom named entity recognition
conditional random field(CRF)
graph database
