面向信息选择的本体构建

L. Khan, Feng Luo
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引用次数: 167

摘要

数字媒体领域的技术产生了大量的非文本信息,音频、视频和图像,以及更熟悉的文本信息。交换和检索信息的潜力是巨大而令人生畏的。实现高效和用户友好检索的关键问题是开发一种搜索机制,以保证提供最少的不相关信息(高精度),同时确保不忽略相关信息(高召回率)。传统的解决方案采用基于关键字的搜索。检索的文档只有那些包含用户指定关键字的文档。但是许多文档传达所需的语义信息而不包含这些关键字。人们可以通过根据意义而不是单词对文档进行索引来克服这个问题,尽管这将需要一种将单词转换为含义和创建本体的方法。通过使用领域相关本体设计和实现基于概念的模型,解决了索引结构的问题。本体是概念及其相互关系的集合,它提供了应用程序领域的抽象视图。为了使我们的方法具有可扩展性,我们提出了一种新的自动生成本体的机制。为此,我们修改了现有的自组织树算法(SOTA),该算法从上到下构建了一个层次结构。此外,为了为层次结构中的每个节点找到合适的概念,我们提出了一种叫做语言本体的WordNet概念自动选择算法。为了说明我们的自动本体构建方法的有效性,我们对文本文档中的本体构建进行了探索。使用了Reuters21578文本文档语料库。我们已经观察到,我们改进的SOTA优于层次聚集聚类(HAC)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ontology construction for information selection
Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).
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