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

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.

arXiv commentKDD 2020 Research Track

References in corpus (3)

• Discriminative Topic Mining via Category-Name Guided Text Embedding
• Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
• Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion

Cited by in corpus (7)

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• A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges
• GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling
• generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation