A PAM-based ontology concept and hierarchy learning method

A PAM-based ontology concept and hierarchy learning method
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基于PAM的本体概念和层次学习方法

DOI:
10.1177/0165551513507406
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发表时间:
2014-02
影响因子:
2.4
通讯作者:
Zhao, Xiaodong
Zhao, Xiaodong
中科院分区:
计算机科学3区
文献类型:
--
作者:
Yu, Miaomiao;Wang, Junli;Zhao, Xiaodong

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本体作为一种能够表达知识的共享概念模型和能够在语义和知识层面描述概念模型的建模工具,在语义网、自然语言处理和信息检索等相关领域发挥着重要作用。本体学习是自动或半自动地构建本体的一系列方法和技术。概念学习和层次学习是本体构建的重要组成部分。提出了一种基于柏青哥分配模型的本体概念和层次学习方法。通过建立本体概念学习模型,将上述问题转化为概率统计推理问题。采用吉布斯抽样对参数进行估计。然后,利用基于WordNet的本体概念生成算法,得到本体概念的抽象描述。在标准测试数据集上的实验结果表明,该方法可以有效地解决本体概念和层次学习问题。
As a shared conceptual model that can express knowledge and a modelling tool that can describe conceptual model in the semantic and knowledge level, ontology plays an important role in the related fields of the Semantic Web, natural language processing and information retrieval. Ontology learning is a series of methods and technologies to construct ontology automatically or semi-automatically. Concept and hierarchy learning are the most important parts of the ontology construction. This paper proposes an ontology concept and hierarchy learning method based on the Pachinko Allocation Model. The above problem is transformed into a probability and statistical inference problem by building an ontology concept learning model. Gibbs sampling is used to estimate the parameters. Then, using the ontology concept generation algorithm based on WordNet, an abstract description of the ontology concept is obtained. Experimental results on the standard test dataset show that the proposed method can offer an effective solution to ontology concept and hierarchy learning.
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