Sequence-to-Sequence Knowledge Graph Completion and Question Answering
Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors. These methods have recently been applied to KG link prediction and question answering over incomplete KGs (KGQA). KGEs typically create an embedding for each entity in the graph, which results in large model sizes on real-world graphs with millions of entities. Their atomic entity representation also necessitates a multi-stage approach to downstream tasks, which limits their utility. We show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model obtaining state-of-the-art results for KG link prediction and KGQA. We achieve this by posing KG link prediction as a sequence-to-sequence task and exchange the triple scoring approach taken by prior KGE methods with a generative decoding approach. Such a simple but powerful method reduces the model size up to 90% compared to conventional KGE models and attains the best performance among small-sized models. An ensemble with a traditional KGE model even sets a new state-of-the-art. After finetuning this model on the task of KGQA over incomplete KGs, our approach outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning.
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DecoderGraph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink PredictionQuestion AnsweringMethods 이 논문이 사용한 방법론
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