Given the overwhelming amounts of information in our current 24/7 stream of new incoming articles, new techniques are needed to enable users to focus on just the key entities and concepts along with their relationships. Examples include news articles but also business reports and social media. The fact that relevant information may be distributed across diverse sources makes it particularly challenging to identify relevant connections. In this paper, we propose a system called MuReX to aid users in quickly discerning salient connections and facts from a set of related documents and viewing the resulting information as a graph-based visualization. Our approach involves open information extraction, followed by a careful transformation and filtering approach. We rely on integer linear programming to ensure that we retain only the most confident and compatible facts with regard to a user query, and finally apply a graph ranking approach to obtain a coherent graph that represents meaningful and salient relationships, which users may explore visually. Experimental results corroborate the effectiveness of our proposed approaches, and the local system we developed has been running for more than one year.

中文翻译：

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用于新闻分析的多文档语义关系提取

鉴于我们当前24/7全天候的新来货中有大量信息，因此需要新技术来使用户专注于关键实体和概念及其关系。示例包括新闻文章，还包括商业报告和社交媒体。相关信息可能分布在各种来源中这一事实使得识别相关联系特别具有挑战性。在本文中，我们提出了一个名为MuReX的系统帮助用户从一组相关文档中快速识别出显着的联系和事实，并以基于图形的可视化形式查看结果信息。我们的方法涉及开放信息提取，然后是仔细的转换和过滤方法。我们依靠整数线性规划来确保我们仅保留关于用户查询的最自信和兼容的事实，并最终应用图排名方法来获得表示有意义和显着关系的连贯图，用户可以在视觉上进行探索。实验结果证实了我们提出的方法的有效性，并且我们开发的本地系统已经运行了一年多。