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Taxonomies have been widely used in various machine learning and text miningsystems to organize knowledge and facilitate downstream tasks. One criticalchallenge is that, as data and business scope grow in real applications,existing taxonomies need to be expanded to incorporate new concepts. Previousworks on taxonomy expansion process the new concepts independently andsimultaneously, ignoring the potential relationships among them and theappropriate order of inserting operations. However, in reality, the newconcepts tend to be mutually correlated and form local hypernym-hyponymstructures. In such a scenario, ignoring the dependencies of new concepts andthe order of insertion may trigger error propagation. For example, existingtaxonomy expansion systems may insert hyponyms to existing taxonomies beforetheir hypernym, leading to sub-optimal expanded taxonomies. To complementexisting taxonomy expansion systems, we propose TaxoOrder, a novelself-supervised framework that simultaneously discovers the localhypernym-hyponym structure among new concepts and decides the order ofinsertion. TaxoOrder can be directly plugged into any taxonomy expansion systemand improve the quality of expanded taxonomies. Experiments on the real-worlddataset validate the effectiveness of TaxoOrder to enhance taxonomy expansionsystems, leading to better-resulting taxonomies with comparison to baselinesunder various evaluation metrics.

Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion