从概念格的概念矩阵出发,提出一种运用全1概念矩阵来生成概念格的算法(Algorithm of Generating Concept LatticeUsing Universal M atrix,GCL1).对整体概念格的形式背景采用0-1矩阵来表达,扫描形式背景的行和列找出全部的全1矩阵,定义...从概念格的概念矩阵出发,提出一种运用全1概念矩阵来生成概念格的算法(Algorithm of Generating Concept LatticeUsing Universal M atrix,GCL1).对整体概念格的形式背景采用0-1矩阵来表达,扫描形式背景的行和列找出全部的全1矩阵,定义了最大秩全1矩阵的概念,并且证明了最大秩全1矩阵对应的结点一定是概念格中的概念;然后按全1矩阵的秩从大到小排序,并对非最大秩的全1矩阵进行扩充,从而得到概念结点,再对概念结点连接,分别建立子概念格;最后把这些子概念格合并生成整体概念格,并同时生成哈斯图.本文对所提出的GCL1算法进行了理论论证,并且通过实例运行,结果表明该算法的时间复杂度明显优于其它许多算法.展开更多
In order to solve the ambiguity problems in the semantic context （structure, granularity or scale） emerging in the process of ontology integration application, this paper analyzes the essential characters of context...In order to solve the ambiguity problems in the semantic context （structure, granularity or scale） emerging in the process of ontology integration application, this paper analyzes the essential characters of context structure, proposes a novel semantic context generating algorithm, which is implemented over VO-Editor（visual ontology editor）, from the satisfiability-based point of view, and proves that the context entity generated by this algorithm is smallest in scale and unique. It offers a feasible means for developers to handle context problems for ontology integration application.展开更多
文摘从概念格的概念矩阵出发,提出一种运用全1概念矩阵来生成概念格的算法(Algorithm of Generating Concept LatticeUsing Universal M atrix,GCL1).对整体概念格的形式背景采用0-1矩阵来表达,扫描形式背景的行和列找出全部的全1矩阵,定义了最大秩全1矩阵的概念,并且证明了最大秩全1矩阵对应的结点一定是概念格中的概念;然后按全1矩阵的秩从大到小排序,并对非最大秩的全1矩阵进行扩充,从而得到概念结点,再对概念结点连接,分别建立子概念格;最后把这些子概念格合并生成整体概念格,并同时生成哈斯图.本文对所提出的GCL1算法进行了理论论证,并且通过实例运行,结果表明该算法的时间复杂度明显优于其它许多算法.
基金the National Natural Science Foundation of China ( 90604005)
文摘In order to solve the ambiguity problems in the semantic context （structure, granularity or scale） emerging in the process of ontology integration application, this paper analyzes the essential characters of context structure, proposes a novel semantic context generating algorithm, which is implemented over VO-Editor（visual ontology editor）, from the satisfiability-based point of view, and proves that the context entity generated by this algorithm is smallest in scale and unique. It offers a feasible means for developers to handle context problems for ontology integration application.
