Related papers: STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

URL: http://arxiv.org/abs/2006.10217v1
Date: Thu, 18 Jun 2020 00:32:53 GMT
Title: STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Authors: Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun and Chao Zhang
Abstract summary: We propose a self-supervised taxonomy expansion model named STEAM. STEAM generates natural self-supervision signals, and formulates a node attachment prediction task. Experiments show STEAM outperforms state-of-the-art methods for taxonomy expansion by 11.6% in accuracy and 7.0% in mean reciprocal rank.
Score: 53.45704816829921
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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}.

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