Toward Degree Bias in Embedding-Based Knowledge Graph Completion
- URL: http://arxiv.org/abs/2302.05044v1
- Date: Fri, 10 Feb 2023 04:14:45 GMT
- Title: Toward Degree Bias in Embedding-Based Knowledge Graph Completion
- Authors: Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang
- Abstract summary: Degree bias can affect graph algorithms by learning poor representations for lower-degree nodes.
In this paper, we validate the existence of degree bias in embedding-based knowledge graphs and identify the key factor to degree bias.
We then introduce a novel data augmentation method, KG-Mixup, to generate synthetic triples to mitigate such bias.
- Score: 37.270356897629675
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A fundamental task for knowledge graphs (KGs) is knowledge graph completion
(KGC). It aims to predict unseen edges by learning representations for all the
entities and relations in a KG. A common concern when learning representations
on traditional graphs is degree bias. It can affect graph algorithms by
learning poor representations for lower-degree nodes, often leading to low
performance on such nodes. However, there has been limited research on whether
there exists degree bias for embedding-based KGC and how such bias affects the
performance of KGC. In this paper, we validate the existence of degree bias in
embedding-based KGC and identify the key factor to degree bias. We then
introduce a novel data augmentation method, KG-Mixup, to generate synthetic
triples to mitigate such bias. Extensive experiments have demonstrated that our
method can improve various embedding-based KGC methods and outperform other
methods tackling the bias problem on multiple benchmark datasets.
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