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학술지 Ontology Kernel - A Convolution Kernel for Ontology Alignment
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저자
손정우, 윤희근, 박성배
발행일
201503
출처
Journal of Information Science and Engineering, v.31 no.2, pp.415-432
ISSN
1016-2364
출판사
Academia Sinica
DOI
https://dx.doi.org/10.1688/JISE.2015.31.2.4
협약과제
14MR9400, (통합)방송용 영상 인식 기반 객체 중심 지식 융합 미디어 서비스 플랫폼 개발, 조기성
초록
Every ontology entity such as a concept or a property has its own structural information represented as a graph due to the relations with other entities. Therefore, it is important to consider not only its lexical similarity but also structural similarity in ontology alignment. This paper proposes ontology kernel that computes both types of similarities simultaneously. The idea of this kernel is to measure the structural similarity of ontology entities by mapping their entity graphs into the space spanned by entity random walks. The graph of an entity in the kernel expresses all relations with other entities. Thus, the ontology kernel can compare the similarity between entities no matter how complex the entities are and no matter how many kinds of relations they possess. A series of experiments with the standard data sets prove the generality and the superiority of the ontology kernel in ontology alignment.
키워드
Convolution kernel, Graph similarity, Hyper graph, Ontology alignment, Ontology kernel
KSP 제안 키워드
Data sets, Graph Similarity, Hyper Graph, Lexical Similarity, Ontology Alignment, Random walk, Structural information, Structure Similarity Index measure(SSIM), convolution kernel