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A Nested Dual Encoder-Decoder Representation Model Based on Entity-Relation Interaction Effects for Knowledge Graph Link Prediction
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AI
DOI：10.1002/cpe.70253.png)
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Abstract
En 中文
Knowledge graph embedding (KGE) offers a more intuitive approach to discovering potential relations between known entities. However, current models are associated with challenges such as a large number of training parameters and low training efficiency and fail to provide in-depth analysis of the impact of embedding dimensionality on entities and relations in link prediction performance. Therefore, we investigate the impact of entity and relation embedding dimensions on their interaction and assess how these dimensions affect the performance of KGE models. Based on these insights, we propose a novel dual encoder-decoder model, NDcRE, which includes decoders MlpD and AttnMlpD, designed to capture long-distance interactions and improve link prediction performance with fewer parameters. Evaluated on four benchmarks, WN18RR, FB15k-237, DB100k, and YAGO3-10, NDcRE significantly improves model efficiency by utilizing fewer parameters and dimensions, thereby enhancing both its utility and convenience. In particular, the AttnMlpD decoder further reduces the model's training parameters, enabling it to deliver strong performance even in environments with limited computational resources.
Keywords:
entity-relation interaction
knowledge graph
knowledge graph completion
knowledge graph embedding
Journal
C
IF:
1.5
Papers:
473
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0