Temporal Interaction Embedding for Link Prediction in Global News Event Graph

Abstract

Global news events graphs (GNEG) are designed for the noisy and ungrammatical world's news media, aiming at capturing the true insight and providing explanations by incorporating potential dimensions and network structures of global news. This article focuses on the temporal representation learning of GNEG to eliminate misunderstanding or ambiguity caused by missing information. Although some temporal models have been developed, the crossover interactions among entity, relation, and time have not been explicitly discussed. The multidirectional effects between entities, relations, and timestamps matter in predicting the establishment of quadruples. This motivates the proposal of learning temporal interaction embeddings (TIE) to benefit GNEG link prediction performance. Specifically, we propose the following. 1) We propose a crossover convolution layer to learn the two-by-two and common interaction features of entity, relation, and time in GNEG to capture their potential effect patterns in the context of different quadruples. 2) For the learned interaction information, we adopt tensor neural network (TNN) to maintain the multiple order structure and further extract effective features to improve prediction. 3) A tensor temporal consistency constraint (TCC) is proposed to enhance the learning of time-weakly sensitive information and induce the embeddings to have a certain compatibility over time. Finally, we carried out extensive experiments on three benchmark datasets, the results proved that the performance of the proposed TIE model is competitive with the state-of-the-art methods.