HapPenIng：发生、预测、推断--知识图谱中的事件序列补全
HapPenIng: Happen, Predict, Infer -- Event Series Completion in a Knowledge Graph
摘要：
温布尔登网球公开赛和美国总统选举等一系列事件代表了体育、文化和政治等关键社会领域的重要事件。然而，语义参考源，如Wikidata，DBpedia和EventKG知识图谱，只提供了一个不完整的事件系列表示。本文研究了知识图谱中的事件序列补全问题。我们处理两项任务：1）子事件关系的预测，以及2）作为事件系列的一部分发生并且在知识图谱中缺失的真实世界事件的推断。为了解决这些问题，我们提出的监督HappPenIng方法利用事件系列的结构特征。HappPenIng不需要任何外部知识--这些特征使它在事件推理的上下文中独一无二。我们的实验评估表明，HappPenIng在子事件预测和推理任务的精度方面分别比基线高出44和52个百分点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
