Networks of Names: Visual Exploration and Semi‐Automatic Tagging of Social Networks from Newspaper Articles
Networks of Names: Visual Exploration and Semi‐Automatic Tagging of Social Networks from Newspaper Articles
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名称网络：报纸文章中社交网络的视觉探索和半自动标记
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Chris Biemann
中科院分区：
文献类型：
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作者：
Artjom Kochtchi;T. V. Landesberger;Chris Biemann
通过阅读报纸文章来理解人与组织之间的关系对于人类来说是难以管理的，因为数据量很大。为了解决这个问题，我们提出并评估了一个新的视觉分析系统，它提供了互动的探索和标记的社交网络从报纸中提取。对于网络的视觉探索，我们使用基于边缘而不是节点的新的兴趣度（DOI）度量来提取节点的“有趣”邻域。它改进了DOI的开创性定义，我们发现它在我们的用例中产生了相同的“全局感兴趣”的邻域，而不管查询如何。我们的方法允许适当地回答不同的用户查询，避免统一的搜索结果。
Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract “interesting” neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same “globally interesting” neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results.