CAJ | 학술논문

原子分解是理解本体内部模块结构的有效途径。以局部化模块抽取为基本操作的原子分解方法可用于强表达力的SROIQ本体，但效率较低。基于有向超图的本体模型能够显式地反映原子的依赖关系，但只局限于弱表达力的EL本体。提出一种混合的原子分解算法，首先利用有向超图表示EL子本体，形成部分原子分解，利用模块抽取方法添加剩余非EL公理，得到本体的全部原子分解。以生物医学本体作为测试数据，实验表明，这种混合的原子分解算法能够有效减少运行时间。与传统的基于模块抽取的方法相比，原子分解效率平均提高6.7倍。
원자분해시리해본체내부모괴결구적유효도경。이국부화모괴추취위기본조작적원자분해방법가용우강표체력적SROIQ본체，단효솔교저。기우유향초도적본체모형능구현식지반영원자적의뢰관계，단지국한우약표체력적EL본체。제출일충혼합적원자분해산법，수선이용유향초도표시EL자본체，형성부분원자분해，이용모괴추취방법첨가잉여비EL공리，득도본체적전부원자분해。이생물의학본체작위측시수거，실험표명，저충혼합적원자분해산법능구유효감소운행시간。여전통적기우모괴추취적방법상비，원자분해효솔평균제고6.7배。
Atomic decomposition is an important approach for understanding the inner structure of ontology. The traditional approach of atomic decomposition, in which the local module extraction is basic operation, is able to decompose expressive SROIQ ontology, however, this approach is inefficient. The direct hypergraph model for ontology represents the dependency relationship of atoms explicitly, it is restricted to an inexpensive EL ontology. In this paper, a hybrid approach is proposed for atomic decomposition. First, the EL subontology is presented in direct hypergraph which forms a partial atomic decom-position for ontology. Then, the remainder non-EL axioms are added into the existential atomic decomposition to obtain the complete decomposition. An empirical evaluation of the algorithm on biomedical ontologies confirms a significant improvement in running time. An average speedup of 6.7-fold is achieved compared to the traditional approach.