Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process
Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process
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DOI：
10.1016/j.proeng.2012.01.594
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Maryam Hourali;G. Montazer
中科院分区：
文献类型：
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作者：
Maryam Hourali;G. Montazer
本体论是语义网的基本基石之一。本体在信息共享和知识管理中的普遍使用需要高效且有效的本体开发方法。本体学习旨在自动或半自动地从各种形式的数据中发现本体知识，可以克服本体开发中本体获取的瓶颈。本文提出了一种新颖的本体学习自动化方法。首先，收集领域相关文档。其次，采用C值方法从文档中提取有意义的术语。然后，使用ART神经网络对文档进行聚类，并通过TF-IDF方法计算术语权重，以找到每个聚类的候选关键词。接下来，应用贝叶斯网络和词汇句法模式来构建初始本体。最后，根据专家的意见对所提出的本体进行评估，并使用本体进行查询扩展。初步结果表明，所提出的本体学习方法比同类研究具有更高的精度。
Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semiautomatically, can overcome the bottleneck of ontology acquisition in ontology development.. In this article a novel automated method for ontology learning is proposed. First, domain-related documents were collected. Secondly, the C-value method was implemented for extracting meaningful terms from documents. Then, an ART neural network was used to cluster documents, and terms’ weight was calculated by TF–IDF method in order to find candidate keyword for each cluster. Next, the Bayesian network and lexico-syntactic patterns were applied to construct the initial ontology. Finally, the proposed ontology was evaluated by expert's views and using the ontology for query expansion purpose. The primary results show that the proposed ontology learning method has higher precision than similar studies.