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

TL;DR

CoRel is a novel seed-guided taxonomy construction method that leverages relation transferring and concept learning to generate more complete, semantically rich topical taxonomies tailored to user interests.

Contribution

It introduces a framework combining relation transferring and concept learning modules to build user-specific topical taxonomies from corpora and seed concepts.

Findings

Outperforms baseline methods significantly in quality.

Generates more complete and semantically rich taxonomies.

Effectively incorporates user interests into taxonomy construction.

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.