Leave-two-out stability of ontology learning algorithm

Jianzhang Wu, Xiao Yu, Linli Zhu and Wei Gao

Chaos, Solitons & Fractals, 2016, vol. 89, issue C, 322-327

Abstract: Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this paper is to study the leave-two-out stability of ontology learning algorithm. Several leave-two-out stabilities are defined in ontology learning setting and the relationship among these stabilities are presented. Furthermore, the results manifested reveal that leave-two-out stability is a sufficient and necessary condition for ontology learning algorithm.

Keywords: Ontology; Similarity measure; Learning algorithm; Ontology loss function; Stability (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:89:y:2016:i:c:p:322-327

DOI: 10.1016/j.chaos.2015.12.013

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