本文選題：粒計(jì)算 + 本體學(xué)習(xí)�。� 參考：《計(jì)算機(jī)工程與應(yīng)用》2017年01期

【摘要】：粒計(jì)算的核心是粒子、粒層和粒結(jié)構(gòu)。應(yīng)用粒計(jì)算思想和本體論模型定義了本體粒和相容粒概念,給出了計(jì)算本體粒集和本體樹的生成算法。該算法通過相容類產(chǎn)生初始本體粒集,應(yīng)用本體粒的內(nèi)涵分量IG擴(kuò)展其他本體粒,最后聯(lián)合關(guān)系分量RG和本體粒的關(guān)系構(gòu)建本體粒集的格分層結(jié)構(gòu),借助加權(quán)樹思想生成本體的概念樹模型。經(jīng)過中醫(yī)喘證本體的實(shí)證研究,說明算法正確高效,為本體學(xué)習(xí)提供了很好的技術(shù)途徑。
[Abstract]:The core of particle computing is particle, granular layer and grain structure. The concepts of ontology particle and compatible grain are defined by using the idea of granular computing and ontology model, and the algorithm of generating computational ontology particle set and ontology tree is given. In this algorithm, the initial ontology grains are generated by compatible classes, and other ontology grains are extended by using the connotation component IG of ontology grains. Finally, the lattice hierarchical structure of ontology grains is constructed by combining the relationship between relational components RG and ontology grains. The concept tree model of ontology is generated by the idea of weighted tree. Through the empirical study of TCM wheezing syndrome ontology, it shows that the algorithm is correct and efficient, and provides a good technical approach for ontology learning.
【作者單位】： 華北理工大學(xué)理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(No.61370168) 河北省自然科學(xué)基金(No.F2014209238)
【分類號(hào)】：TP18

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