Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
arXiv (Cornell University)(2023)
摘要
Knowledge graph embedding models (KGEMs) are used for various tasks related
to knowledge graphs (KGs), including link prediction. They are trained with
loss functions that consider batches of true and false triples. However,
different kinds of false triples exist and recent works suggest that they
should not be valued equally, leading to specific negative sampling procedures.
In line with this recent assumption, we posit that negative triples that are
semantically valid w.r.t. signatures of relations (domain and range) are
high-quality negatives. Hence, we enrich the three main loss functions for link
prediction such that all kinds of negatives are sampled but treated differently
based on their semantic validity. In an extensive and controlled experimental
setting, we show that the proposed loss functions systematically provide
satisfying results which demonstrates both the generality and superiority of
our proposed approach. In fact, the proposed loss functions (1) lead to better
MRR and Hits@10 values, and (2) drive KGEMs towards better semantic correctness
as measured by the Sem@K metric. This highlights that relation signatures
globally improve KGEMs, and thus should be incorporated into loss functions.
Domains and ranges of relations being largely available in schema-defined KGs,
this makes our approach both beneficial and widely usable in practice.
更多查看译文
关键词
enriching loss functions,prediction,different negatives,range constraints
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要