基于数据场和全局序列比对的大规模中文关联数据模型

目前关联数据的研究工作主要集中在实例级别上展开,而在模式级别(Schema-Level)上的关联数据构建则易被忽视。本体映射是解决本体异构问题的重要途径和手段,同时,本体映射也可视为模式级别关联数据构建的典型情景。特别是在中文知识库方面,中文知识是关联数据网中的重要组成部分,但现有的中文本体映射系统在面对大规模本体映射任务时,显得效率较低且可用性不高,目前仍缺乏针对中文大规模本体映射的相关系统。为了解决在模式级别上的中文大规模关联数据构建问题,提出了一种新的基于数据场和序列比对思想的大规模中文关联数据构建模型。首先,基于改进的融合概念相似度和相异度的拟核力场势函数对大规模中文本体映射规模进行约简和压缩;其次,通过引入序列比对算法,对组合概念进行相似度的度量;最后,将本系统与相似度计算相关典型算法进行比较,表明其具备一定的可用性和较高的总体性能。

A Schema-Level Ontology Alignment Model for Chinese Linked Open Data

The current research on the linked open data(LOD) mainly focused on level of instances,while the task on finding schema-level links between LOD datasets is ignored. In order to solve the large-scale Chinese ontology mapping problem occurred in LOD,we propose a data field and sequence alignment-based ontology mapping architecture. Firstly,based on an improved nuclear field potential function,we compress dimension of unaligned large-scale Chinese ontology. Secondly,we use the sequence alignment algorithm to compute similarity between concepts. Compared to other typical similarity computing algorithms,the experimental results show that the proposed method has higher overall performance and usability.