I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning

• URL: http://arxiv.org/abs/2208.09828v1
• Date: Sun, 21 Aug 2022 07:34:37 GMT
• Title: I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
• Authors: Yang Liu and Zequn Sun Guangyao Li and Wei Hu
• Abstract summary: Knowledge graph embedding seeks to learn vector representations for entities and relations. Recent studies have used pre-trained language models to learn embeddings based on the textual information of entities and relations. We propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complement of graph structures and text information.
• Score: 16.723470319188102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graph (KG) embedding seeks to learn vector representations for entities and relations. Conventional models reason over graph structures, but they suffer from the issues of graph incompleteness and long-tail entities. Recent studies have used pre-trained language models to learn embeddings based on the textual information of entities and relations, but they cannot take advantage of graph structures. In the paper, we show empirically that these two kinds of features are complementary for KG embedding. To this end, we propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complementarity of graph structures and text information. Its graph embedding model employs Transformer to reconstruct the representation of an entity from its neighborhood subgraph. Its text embedding model uses a pre-trained language model to generate entity representations from the soft prompts of their names, descriptions, and relational neighbors. To let the two model promote each other, we propose co-distillation learning that allows them to distill selective knowledge from each other's prediction logits. In our co-distillation learning, each model serves as both a teacher and a student. Experiments on benchmark datasets demonstrate that the two models outperform their related baselines, and the ensemble method CoLE with co-distillation learning advances the state-of-the-art of KG embedding.

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