Cai Yi, Xie Haoran, Lau Raymond Y K, Li Qing, Wong Tak-Lam, Wang Fu Lee
School of Software Engineering, South China University of Technology, China.
Department of Computing and Decision Sciences, Lingnan University, Hong Kong.
Appl Soft Comput. 2019 Dec;85:105750. doi: 10.1016/j.asoc.2019.105750. Epub 2019 Sep 25.
To satisfy a user's need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships: temporal, content dependence, and event reference, that can be used to identify to what extent a component event is dependent on another in the evolution of a target event (i.e., the query event). The search results are organized as a temporal event map (TEM) that serves as the whole picture about an event's evolution or development by showing the dependence relationships among events. Based on the event relationships in the TEM, we further propose a method to measure the degrees of importance of events, so as to discover the important component events for a query, as well as the several algebraic operators involved in the TEM, that allow users to view the target event. Experiments conducted on a real data set show that our method outperforms the baseline method Event Evolution Graph (EEG), and it can help discover certain new relationships missed by previous methods and even by human annotators.
为了有效且高效地满足用户查找并理解事件全貌的需求，本文将时间事件搜索问题形式化，并基于用户查询提出了一个用于搜索事件的事件关系分析框架。我们定义了三种事件关系：时间关系、内容依赖关系和事件引用关系，这些关系可用于确定在目标事件（即查询事件）的演变过程中，一个子事件在多大程度上依赖于另一个子事件。搜索结果被组织成一个时间事件图（TEM），该图通过展示事件之间的依赖关系来呈现事件演变或发展的全貌。基于TEM中的事件关系，我们进一步提出了一种衡量事件重要程度的方法，以便发现查询的重要子事件，以及TEM中涉及的几个代数运算符，这些运算符允许用户查看目标事件。在真实数据集上进行的实验表明，我们的方法优于基线方法事件演化图（EEG），并且它可以帮助发现先前方法甚至人类注释者遗漏的某些新关系。
