發(fā)布時間：2018-08-02 18:31

【摘要】：以農(nóng)業(yè)AGROVOC本體為例,結(jié)合語義萬維網(wǎng)推理機制定性分析冗余信息產(chǎn)生原因,利用復(fù)雜網(wǎng)絡(luò)分析方法量化推理過程中產(chǎn)生的冗余,進而確定本體中的核心概念,解決推理冗余問題。實驗表明,復(fù)雜網(wǎng)絡(luò)分析方法可從定量角度找出核心節(jié)點及導(dǎo)致推理產(chǎn)生冗余的邊,并揭示了語義冗余引起的推理效率降低問題。為優(yōu)化本體設(shè)計、提高推理效率提供了一種新的可行方法。
[Abstract]:Taking agricultural AGROVOC ontology as an example, combining with semantic World wide Web reasoning mechanism to qualitatively analyze the causes of redundant information, this paper uses complex network analysis method to quantify the redundancy in reasoning process, and then determines the core concepts of ontology. Solve the problem of reasoning redundancy. The experiments show that the complex network analysis method can find out the core nodes and the edges that lead to the redundancy of reasoning from the quantitative point of view, and reveal the problem of reducing the efficiency of reasoning caused by semantic redundancy. It provides a new feasible method for optimizing ontology design and improving reasoning efficiency.
【作者單位】： 西南大學(xué)計算機與信息科學(xué)學(xué)院;西南大學(xué)智能軟件與軟件工程重點實驗室;
【基金】：國家高技術(shù)研究發(fā)展計劃項目(2013AA013801) 國家自然科學(xué)基金(61402379,61403315) 重慶市研究生科研創(chuàng)新項目(CYS14063)
【分類號】：TP393.0;O157.5
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本文編號：2160329