Purpose

With the wealth of information available on the World Wide Web, it is difficult for anyone from a general user to the researcher to easily fulfill their information need. The main challenge is to categorize the documents systematically and also take into account more valuable data such as semantic information. The purpose of this paper is to develop a concept-based search system that leverages the external knowledge resources as the background knowledge for getting the accurate and efficient meaningful search results.

Design/methodology/approach

The paper introduces the approach which is based on formal concept analysis (FCA) with the semantic information to support the document management in information retrieval (IR). To describe the semantic information of the documents, the system uses the popular knowledge resources WordNet and Wikipedia. By using FCA, the system creates the concept lattice as the concept hierarchy of the document and proposes the navigation algorithm for retrieving the hierarchy based on the user query.

Findings

The semantic information of the document is based on the two external popular knowledge resources; the authors find that it will be more efficient to deal with the semantic mismatch problems of user need.

Originality/value

The navigation algorithm proposed in this research is applied to the scientific articles of the National Science Foundation (NSF). The proposed system can enhance the integration and exploration of the scientific articles for the advancement of the Scientific and Engineering Research Community.

中文翻译：

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基于内容分析的文档和研究文章的探索

目的

借助万维网上的大量信息，从普通用户到研究人员，任何人都很难轻松满足他们的信息需求。主要挑战是系统地对文档进行分类，并考虑更有价值的数据，例如语义信息。本文的目的是开发一种基于概念的搜索系统，该系统利用外部知识资源作为背景知识，以获得准确有效的有意义的搜索结果。

设计/方法/方法

本文介绍了基于形式概念分析（FCA）的语义信息支持信息检索（IR）中的文档管理的方法。为了描述文档的语义信息，系统使用了流行的知识资源 WordNet 和 Wikipedia。系统利用FCA创建概念格作为文档的概念层次结构，并提出基于用户查询的层次结构检索导航算法。

发现

文档的语义信息基于两个外部流行知识资源；作者发现处理用户需求的语义不匹配问题会更有效。

原创性/价值

本研究提出的导航算法应用于美国国家科学基金会（NSF）的科学文章。所提出的系统可以增强科学文章的整合和探索，以促进科学和工程研究社区的发展。