Term-Centric Hierarchy Induction from Heterogeneous Corpora
For researchers and practitioners in policy analysis, innovation monitoring, and domain mapping, this work provides a scalable method for organizing knowledge from diverse sources into interpretable hierarchies.
The paper tackles the problem of inducing hierarchical taxonomies from heterogeneous text corpora, proposing a term-centric framework that improves cross-source coherence and hierarchy quality over baselines, as demonstrated on a multi-source benchmark of over one million documents.
Organizing knowledge from diverse text sources into interpretable hierarchies is crucial for tasks such as policy analysis, innovation monitoring, and exploratory domain mapping. Existing taxonomy induction methods typically rely on document-level representations that capture entire documents rather than the specific domain concepts relevant for knowledge organization, limiting their ability to generalize across heterogeneous sources. We propose a term-centric framework for inducing hierarchical taxonomies from heterogeneous corpora that scales to massive document collections. Our approach maps documents from diverse sources into a shared representation space using automatic term extraction, enabling robust cross-source alignment. Based on these representations, we construct interpretable hierarchies that integrate domain priors with datadriven clustering. Experiments on a novel English and German multi-source benchmark of over one million documents demonstrate that our method improves cross-source coherence and hierarchy quality over text- and summarybased baselines. A case study on German regional innovation analysis further demonstrates its practical utility for technology landscape mapping.