Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
翻译:活动系列,如温布尔登锦标赛和美国总统选举,代表了体育、文化和政治等关键社会领域的重要事件。然而,语义参考来源,如维基数据、DBpedia和EnterKG知识图表,仅提供了不完整的事件系列说明。在本文中,我们将事件系列完成问题作为知识图中的目标。我们处理两个任务:(1) 预测次事件关系,(2) 推断作为事件系列的一部分发生的、在知识图中缺失的真实世界事件。为解决这些问题,我们提议的受监督的HapPenIng方法利用了事件系列的结构特征。HapPenIng并不需要任何外部知识,在事件推论中使其独特的特征。我们的实验评估表明,HapenIng在次事件预测和推论任务精确度方面比基线高出44和52个百分点。