QAM:基于知识图谱的军事问答系统

Xueling Dai, Jike Ge, Hongyue Zhong, Dong Chen, Jun Peng
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引用次数: 1

摘要

为了自动从互联网上收集信息并提供给用户查询,现有的许多搜索引擎都集中在大量和杂乱的信息上。然而,我们注意到,搜索引擎对特定问题产生的大量答案几乎没有准确性或智能。因此,我们考虑了基于知识图(KG)的问答系统方法。最近的作品被广泛分享,KG可以深入到医学和金融等领域。随着技术的飞速发展,军队迫切需要质量保证系统。本文提出了一种基于军用KG (QAM)的质量保证系统。它应用语义网技术,并允许在表示和审讯级别对军事问题和文件进行深入分析。实验结果表明,将KG应用于军事领域,可以弥补一般搜索引擎的不足,返回精炼准确的搜索结果,为军事领域提供便捷的知识服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QAM: Question Answering System Based on Knowledge Graph in the Military
To automatically collect information from the Internet and provide it to users for query, much of the existing search engines have been focused on massive and sprawling information. However, we note that the search engines produce a host of answers to specific questions with little accuracy or intelligence. Therefore, we consider the approach of question answering (QA) systems based on the knowledge graph (KG). Resent works are widely shared that the KG can be deeper for the field of medicine and finance, etc. Nowadays, QA system in the military is sorely needed with the dramatic growth of the technology. In this paper, we propose a QA system based on the KG in the military (QAM). Which applies semantic Web technology, and allows a deep analysis of military questions and documents at both representation and interrogation levels. The experimental results reveal that KG applied in the military domain, can make up the inadequacy of the general search engine, return to refine and accurate results, and provide conveniently knowledge service in the military field.
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