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- W4288102912 abstract "For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations. Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions. We design a novel model to better extract key information from textual descriptions. Besides, we also develop a novel generative model in our framework to enhance the performance by generating extra triplets during the training stage. Experiments are conducted on two datasets from real-world KGs, and the results show that our framework outperforms previous methods when dealing with infrequent relations and their accompanying uncommon entities." @default.
- W4288102912 created "2022-07-28" @default.
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- W4288102912 date "2019-09-25" @default.
- W4288102912 modified "2023-10-17" @default.
- W4288102912 title "Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion" @default.
- W4288102912 doi "https://doi.org/10.48550/arxiv.1909.11359" @default.
- W4288102912 hasPublicationYear "2019" @default.
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