论文标题
蒸汽：与迷你路径的自我监督分类法扩展
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
论文作者
论文摘要
分类法是每天支撑大量申请的重要知识本体，但是实践中使用的许多分类法都遭受覆盖范围低的问题。我们研究了分类法扩展问题，该问题旨在扩大新概念术语的现有分类法。我们提出了一个名为Steam的自制分类学扩展模型，该模型利用现有的分类法进行自然监督进行扩展。为了产生自然的自主信号，蒸汽样本从现有的分类法中采样了迷你录音，并制定了锚定迷路和查询术语之间的节点附件预测任务。为了求解节点附件任务，它从多个视图中学习了查询锚关系对的功能表示，并执行多视图共同训练以进行预测。广泛的实验表明，Steam的分类法扩展的最先进方法的准确性上升了11.6 \％，在三个公共基准上的平均值等级为7.0 \％。可以在\ url {https://github.com/yueyu1030/steam}找到Steam的实现。
Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We study the taxonomy expansion problem, which aims to expand existing taxonomies with new concept terms. We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion. To generate natural self-supervision signals, STEAM samples mini-paths from the existing taxonomy, and formulates a node attachment prediction task between anchor mini-paths and query terms. To solve the node attachment task, it learns feature representations for query-anchor pairs from multiple views and performs multi-view co-training for prediction. Extensive experiments show that STEAM outperforms state-of-the-art methods for taxonomy expansion by 11.6\% in accuracy and 7.0\% in mean reciprocal rank on three public benchmarks. The implementation of STEAM can be found at \url{https://github.com/yueyu1030/STEAM}.
