摘要:基于数据分析的智能决策对提升企业竞争力具有重要意义.根据待分析的问题，从内部信息系统的数据库中查询并获取与问题密切相关且信息完整的数据，是企业数据分析过程中的关键环节.基于本体的可视化数据查询系统为不掌握计算机专业技能的终端用户提供了高效获取数据的手段，近年来成为研究热点.然而现有工作仅采用简单的映射规则，将数据库中的表、字段、外键关系等元素直接映射为本体中的概念、属性和关系，向终端用户暴露了过多数据库设计的技术细节，增加了用户理解的难度，降低了系统的可用性.而通过人工编写映射规则来屏蔽数据库细节，既低效又缺乏通用性.针对这一问题，提出了一种基于推理的终端用户本体查询构造方法.该方法利用本体模型的语义表达能力和推理能力，在原有基于数据库简单映射所生成的本体模型基础上注入领域知识，从而优化查询构造流程，使终端用户得以从其更为熟悉的业务知识的视角，而非数据库设计的视角来看待和操纵数据，提高系统可用性；同时，增加了对分组统计的支持，扩展了方法的适用范围.最后，通过对"餐饮前台信息管理"领域真实案例的分析，验证了该方法相对于已有方法，其可用性提高了53.44%，表达能力提高了20.43%.



Abstract:Intelligent decision-making based on data analysis is of great significance to enhance the competitiveness of enterprises. Querying and obtaining the information and complete data closely related to the problem from the internal information system database, is a key point in enterprise data analysis. The ontology-based visual query system (VQS) provides end-users with an effective way to access data. In recent years, by using the simple mapping rules, the database table, field, the foreign key relations, and other elements is directly mapped to the concept, attributes, and relationships in the ontology. It exposed too much database design technical details to the end user, thus increasing the end users' burden when using the VQS system. Masking database details by manually writing mapping rules is both inefficient and not universal. To this end, this study proposes a reasoning-based end user ontology query construction method. This method uses the semantic expression ability and reasoning ability of the ontology model to inject the domain knowledge into the original ontology model that is directly derived from the database. It optimizes the query construction process and enables the end user to query and manipulate the data from the domain experts' perspective, instead of a database design perspective, which improves system usability. It adds support of the group statistics and extends the application scope of the method. Finally, this method is evaluated by analyzing real cases in the field of "Restaurant Information Management" and the experimental results demonstrate that the proposed approach outperforms other baseline methods. the proposed approach has improved the usability by 53.44% and the expression ability by 20.43%.



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