Storytelling in entity networks to support intelligence analysts
Storytelling in entity networks to support intelligence analysts
复制标题
在实体网络中讲故事以支持情报分析师
DOI：
10.1145/2339530.2339742
复制
发表时间：
2012
影响因子：
22
通讯作者：
Naren Ramakrishnan
中科院分区：
文献类型：
--
作者：
M. S. Hossain;P. Butler;Arnold P. Boedihardjo;Naren Ramakrishnan
情报分析师面临着许多挑战，其中最主要的是需要软件支持讲故事，即，自动地在不同实体之间“连接点”（例如，人、组织）努力形成假设并提出不明显的关系。我们提出了一个系统来自动构建实体网络中的故事，可以帮助形成有向的关系链，支持共同引用，证据编组，并施加语法约束的故事生成过程。一种基于概念格挖掘的新的优化技术使我们能够在海量数据集上快速构建故事。使用几个公共领域的数据集，我们说明了我们的方法如何克服当前系统的许多局限性，使分析师能够有效地缩小到感兴趣的假设和原因的替代解释。
Intelligence analysts grapple with many challenges, chief among them is the need for software support in storytelling, i.e., automatically 'connecting the dots' between disparate entities (e.g., people, organizations) in an effort to form hypotheses and suggest non-obvious relationships. We present a system to automatically construct stories in entity networks that can help form directed chains of relationships, with support for co-referencing, evidence marshaling, and imposing syntactic constraints on the story generation process. A novel optimization technique based on concept lattice mining enables us to rapidly construct stories on massive datasets. Using several public domain datasets, we illustrate how our approach overcomes many limitations of current systems and enables the analyst to efficiently narrow down to hypotheses of interest and reason about alternative explanations.