CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring

• URL: http://arxiv.org/abs/2010.06714v1
• Date: Tue, 13 Oct 2020 22:00:31 GMT
• Title: CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
• Authors: Jiaxin Huang, Yiqing Xie, Yu Meng, Yunyi Zhang, Jiawei Han
• Abstract summary: We propose a method for seed-guided topical taxonomy construction, which takes a corpus and a seed taxonomy described by concept names as input. A relation transferring module learns and transfers the user's interested relation along multiple paths to expand the seed taxonomy structure in width and depth. A concept learning module enriches the semantics of each concept node by jointly embedding the taxonomy.
• Score: 37.1330815281983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search. Most existing taxonomy construction methods extract hypernym-hyponym entity pairs to organize a "universal" taxonomy. However, these generic taxonomies cannot satisfy user's specific interest in certain areas and relations. Moreover, the nature of instance taxonomy treats each node as a single word, which has low semantic coverage. In this paper, we propose a method for seed-guided topical taxonomy construction, which takes a corpus and a seed taxonomy described by concept names as input, and constructs a more complete taxonomy based on user's interest, wherein each node is represented by a cluster of coherent terms. Our framework, CoRel, has two modules to fulfill this goal. A relation transferring module learns and transfers the user's interested relation along multiple paths to expand the seed taxonomy structure in width and depth. A concept learning module enriches the semantics of each concept node by jointly embedding the taxonomy and text. Comprehensive experiments conducted on real-world datasets show that Corel generates high-quality topical taxonomies and outperforms all the baselines significantly.

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