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首页> 外文期刊>International Journal of Applied Research on Information Technology and Computing >Ontology-Based Semantic Learning of Genetic-Algorithm-Optimised Back Propagation Artificial Neural Network for Personalised Web Search
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Ontology-Based Semantic Learning of Genetic-Algorithm-Optimised Back Propagation Artificial Neural Network for Personalised Web Search

机译：基于本体的遗传算法优化反向传播人工神经网络语义学习

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摘要

In this paper, a novel approach is proposed for training of genetic algorithm (GA), back propagation (BP) and artificial neural network (ANN) based on semantic clusters of web query sessions and used as classifierfor web page recommendation. The document concept vector is generated on the basis of one Word Net ontology using word sense ambiguation, and web query session concept vectors are formed for clustering. These semantic clusters of web query sessions are used for GA-based weight optimisation of ANN and training of GA-based weight optimised BP-ANN for classification of web queries to clusters. During web search, users' queries are expanded with related concepts based on Word Net ontology and are classified to clusters using trained GA BP-ANN for recommendation of web pages and customisation of user information search. The experimental results were verified statistically and showed the improvement in precision of recommended search results. Hence, it is concluded that semantic-based learning of GA-BP-ANN improves classification accuracy of web queries to clusters and generates the recommendations of semantically similar web pages for web search personalisation.
机译：本文提出了一种基于Web查询会话语义簇的遗传算法（GA），反向传播（BP）和人工神经网络（ANN）的训练方法，并将其作为网页推荐的分类器。使用词义歧义在一个词网本体的基础上生成文档概念向量，并形成Web查询会话概念向量以进行聚类。 Web查询会话的这些语义集群用于基于GA的ANN权重优化，以及基于GA的权重优化BP-ANN的训练，用于将Web查询分类为集群。在网络搜索过程中，用户查询将基于Word Net本体扩展相关概念，并使用经过训练的GA BP-ANN将其分类为群集，以推荐网页和自定义用户信息搜索。实验结果经过统计验证，并显示了推荐搜索结果的精度提高。因此，可以得出结论，GA-BP-ANN的基于语义的学习提高了对集群的Web查询的分类准确性，并生成了语义相似的Web页面的建议以用于Web搜索个性化。

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