HapPenIng: Happen, Predict, Infer -- Event Series Completion in a Knowledge Graph

TL;DR

HapPenIng is a supervised method for completing event series in knowledge graphs by predicting sub-event relations and inferring missing real-world events without external knowledge, significantly outperforming baselines.

Contribution

The paper introduces HapPenIng, a novel supervised approach that leverages structural features for event series completion without external knowledge.

Findings

HapPenIng outperforms baselines by 44-52 percentage points in precision.

The method effectively predicts sub-event relations.

It accurately infers missing real-world events in knowledge graphs.

Abstract

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.