ISWC 2022
Workshop paper

Knowledge Graph Embeddings for Causal Relation Prediction

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Recently, there has been an increasing interest in knowledge graphs (KGs) of causal relations between events. Such KGs can be used for event analysis and forecasting in a variety of applications. In this paper, we study the problem of enriching an existing causal KG of news events using KG embeddings-based link prediction techniques. We perform a thorough evaluation of the performance of five different methods using classic accuracy measures as well as a novel scheme for manual evaluation. Our study provides insights on the strengths and weaknesses of different link prediction methods.