Dynamic knowledge graph completion of temporal aware combination
A time-aware combination(TAC)method for temporal knowledge graph completion was proposed aiming at the problem that the existing temporal knowledge graph embedding methods only consider the relationship of temporal information or encode independent temporal vectors and the completion performance of these methods is not high enough.The effectiveness of temporal information on knowledge graph completion methods was analyzed by modeling dimensional features.Different learning methods have different effects on the representation learning ability after considering the embedding of temporal information through the embedding method of combining the embedded and independent temporal information.Long short-term memory(LSTM)network was utilized to encode temporal information,learn more accurate temporal dimension features and help to improve the performance of temporal graph.Experiments on ICEWS14,ICEWS05-15 and GDELT datasets verified the effectiveness of the time-aware combination method.The related research performance metrics were compared.Results show that the proposed method performs better in link prediction.
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