Fuzzy Ontology Based Document Feature Vector Modification Using Fuzzy Tree Transducer

Mohammad K. Fallah, S. Moghari, E. Nazemi, M. M. Zahedi
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引用次数: 6

Abstract

Recently, an emphasis has been placed on the content based Information Retrieval Systems (IRS). Finding documents based on content similarity using background knowledge is becoming an increasingly important task. This paper aims for two main tasks in order to high quality document retrieval; first, we present our formulation of fuzzy ontology and then, by this formulation, propose a method which uses two functions for manipulating document feature vector. We describe encoding a fuzzy ontology into a Fuzzy Tree Transducer (FIT) and then, define two simple functions for applying attained FIT on document feature vector. By using the first function, elements of document feature vector are modified to reduce distance between current document and relevant documents in the vector space. This reduction is so important for categorization of documents in index repository of IRSs. The second function uses the injection of relevant context into query term. The injected context causes relocation of query term in vector space, and reduces its distance from some semantically related documents.
基于模糊本体的模糊树换能器文档特征向量修改
近年来,基于内容的信息检索系统(IRS)成为研究的重点。利用背景知识进行基于内容相似度的文档查找已成为越来越重要的任务。为了实现高质量的文献检索,本文主要完成了两个任务;本文首先给出了模糊本体的表述,然后在此基础上提出了一种利用两个函数对文档特征向量进行处理的方法。首先将模糊本体编码为模糊树换能器(FIT),然后定义了两个简单的函数,用于将得到的FIT应用于文档特征向量。利用第一个函数,修改文档特征向量的元素,减小当前文档与相关文档在向量空间中的距离。这种简化对于rss索引存储库中的文档分类非常重要。第二个函数将相关上下文注入到查询项中。注入的上下文导致查询项在向量空间中重新定位,并减少了与一些语义相关文档的距离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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