基于本体知识库的模糊信息检索研究

Study of Fuzzy Information Retrieval Based on Ontology Knowledge Base

信息检索采用知识组织可提高返回语义相关的文档数量与初始用户查询相关度的质量。文章提出的模糊信息检索模型可为信息检索提供一种编码知识库结构,该知识库由多相关本体组成,本体的关系表示为模糊关系。在这种知识组织中使用一种新方法来扩展用户初始查询和索引文档集,独立表示本体以及概念间的关系。实验结果表明,与另一经典的模糊信息检索方法相比,提出的模型具有更好的整体性能比。

By employing the knowledge organization, information retrieval can enhance the quality of returning semantically related documents and the quality of degree of correlation of initial user＇ s query, the proposed fuzzy a framework to encode a knowledge base which is composed of multiple - related ontologies whose relationships are expressed as fuzzy relations. This knowledge organization is used in a novel method to expand the user initial query and to index the documents in the collection, allowing the ontologies as well as the relationships among their concepts to be represented independently. Experimental results show that the proposed model presents better overall performance when compared with another classical fuzzy -based approach for information retrieval.