利用基于知识的网络生态系统的集成搜索框架

Dengya Zhu, S. Nimmagadda, Torsten Reiners, A. Rudra
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引用次数: 2

摘要

信息的爆炸式增长制约了知识网络生态系统相关搜索词的判断,给相关信息的检索和知识管理带来了挑战。需要注意现有的信息检索工具及其在框架内的融合,以便对检索结果进行有效的管理。在本文中,我们将演示各种用户和代理在各种KBWE场景中如何有效地使用信息检索服务。提出了一种利用爬行策略、网络搜索技术和传统数据库搜索方法的集成搜索框架(ISF)。此外,ISF还提供从Internet、extranet、intranet到个人桌面的全面、动态、个性化和面向组织的信息检索服务。在这一实证研究中,实验证明了搜索过程的改进,正如在概念ISF中所识别的那样。实验结果表明,与其他流行的搜索引擎相比,该算法的精度有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem
The explosion of information constrains the judgement of search terms associated with Knowledge-Based Web Ecosystem (KBWE), making the retrieval of relevant information and its knowledge management challenging. The existing information retrieval (IR) tools and their fusion in a framework need attention, in which search results can effectively be managed. In this article, we demonstrate the effective use of information retrieval services by a variety of users and agents in various KBWE scenarios. An innovative Integrated Search Framework (ISF) is proposed, which utilises crawling strategies, web search technologies and traditional database search methods. Besides, ISF offers comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, extranet, intranet, to personal desktop. In this empirical research, experiments are carried out demonstrating the improvements in the search process, as discerned in the conceptual ISF. The experimental results show improved precision compared with other popular search engines.
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