从已有的数据模型中进行语义提取,经过一定的规则映射生成本体的过程称为本体学习.关系数据库模型是当前数据的存取与组织的主要模型,从中学习得到本体,一直是本体工程领域研究的热点之一.利用手工定义的E-R模型到本体的映射规则来完成本体的构建,是国内外大部分学者采用的方法.但这样获得的本体概念层次关系主观依赖性强,不利于本体的实际应用.为了能更加客观地获取数据之间的概念层次关系与语义信息,提出了一种基于FCA(形式概念分析)从关系数据库进行本体学习的方法.该方法既保持了关系数据表中原有的数据语义关系,又发挥了FCA自动提取语义信息的特点,提高了最终本体生成的质量,有利于在具体的领域应用中使用本体.最后结合材料服役安全数据库的数据信息,演示了运用所提出的方法学习得到领域本体的过程.%Ontology learning is a process, which extracts semantic information from the existing data model and generates ontology using a set of predefined mapping rules. Relational database is the main model of data access and management, and extracting ontology from relational database is one of the research hotspots in ontology engineering field. A common method adopted by the domestic and foreign scholars is that ontology is constructed by using mapping rules between E-R model and ontology elements. But this method is subjective and it goes against the application of ontology. To address this issue,an approach of ontology learning from relational database based on formal concept analysis was proposed, which could obtain the hierarchical relation of concept and semantic relation of data objectively. The proposed method not only keeps the semantic information of relational data tables,but also shows the advantage of FCA in automatic extraction of semantic information. Thus the quality of final ontology is improved and the application field of ontology is extended. A case study of the proposed method combined with materials service safety database was also presented.
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