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Intelligent fuzzy information retrieval based on ontology knowledge-base

机译:基于本体知识库的智能模糊信息检索

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摘要

Intelligent fuzzy information retrieval (IR) based on ontology knowledge base has become one of the most active research directions in the field of intelligent IR system. How to use ontology knowledge base to further improve its retrieval performance and intelligence has become the main research goal of intelligent fuzzy IR system based on ontology knowledge-base. IR using knowledge organisation can improve the quality between returning document and user's initial query. The intelligent fuzzy information retrieval model proposed in this paper can provide a coded knowledge-base structure for IR, which is composed of multiple related ontologies, and the relationship between ontology is expressed as fuzzy relationship. In this kind of knowledge organisation, a new method is used to extend user's initial query and index document set, which independently represents ontology and the relationship between concepts. The experimental results show that the proposed model has a better overall performance compared with the other classical IR method. The prospects for future development and suggestions for possible extensions of the ontology knowledge base-based intelligent fuzzy IR systems are also discussed.
机译:基于本体知识库的智能模糊信息检索(IR)已经成为智能IR系统领域最活跃的研究方向之一。如何利用本体知识库进一步提高其检索性能和智能性已成为基于本体知识库的智能模糊红外系统的主要研究目标。使用知识组织的IR可以提高返回文档和用户初始查询之间的质量。本文提出的智能模糊信息检索模型可以为红外提供一个编码的知识库结构,该结构由多个相关本体组成,本体之间的关系表示为模糊关系。在这种知识组织中,使用了一种新方法来扩展用户的初始查询和索引文档集,该方法独立地表示本体和概念之间的关系。实验结果表明,与其他经典的红外方法相比,该模型具有更好的整体性能。还讨论了基于本体知识的智能模糊红外系统的未来发展前景和可能的扩展建议。

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