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Supporting sense-making and decision-making through time evolution analysis of open sources

机译:通过开源的时间演化分析来支持理性决策

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Modern societies produce a huge amount of open source information that is often published on the Web in a natural language form. The impossibility of reading all these documents is paving the way to semantic-based technologies that are able to extract from unstructured documents relevant information for analysts. Most solutions extract uncorrelated pieces of information from individual documents; few of them create links among related documents and, to the best of our knowledge, no technology focuses on the time evolution of relations among entities. We propose a novel approach for managing, querying and visualizing temporal knowledge extracted from unstructured documents that can open the way to novel forms of sense-making and decision-making processes. We leverage state-of-the-art natural language processing engines for the semantic analysis of textual data sources to build a temporal graph database that highlights relationships among entities belonging to different documents and time frames. Moreover, we introduce the concept of temporal graph query that analysts can use to identify all the relationships of an entity and to visualize their evolution over time. This process enables the application of statistical algorithms that can be oriented to the automatic analysis of anomalies, state change detection, forecasting. Preliminary results demonstrate that the representation of the evolution of entities and relationships allows an analyst to highlight relevant events among the large amount of open source documents.
机译:现代社会产生了大量的开源信息,这些信息通常以自然语言形式发布在Web上。阅读所有这些文档的可能性为基于语义的技术铺平了道路,该技术能够从非结构化文档中提取分析人员的相关信息。大多数解决方案都从单个文档中提取不相关的信息。据我们所知,只有极少数的技术在相关文档之间建立链接,没有一种技术专注于实体之间关系的时间演变。我们提出了一种新颖的方法来管理,查询和可视化从非结构化文档中提取的时间知识,这可以为新型的感官和决策流程开辟道路。我们利用最先进的自然语言处理引擎对文本数据源进行语义分析,以建立一个时间图数据库,突出显示属于不同文档和时间范围的实体之间的关系。此外,我们引入了时间图查询的概念,分析人员可以使用该概念来识别实体的所有关系并可视化其随时间的演变。这个过程使统计算法的应用成为可能,这些统计算法可以用于异常的自动分析,状态变化检测,预测。初步结果表明,实体和关系演化的表示使分析师能够突出显示大量开放源代码文档中的相关事件。

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