Term-Centric Hierarchy Induction from Heterogeneous Corpora

2026-06-25

AI Summary

This study addresses the challenge of aligning and interpreting knowledge structures across heterogeneous textual corpora by proposing a term-centric, hierarchical knowledge construction framework. Departing from conventional full-document representations, the approach maps multi-source documents into a shared semantic space through automated term extraction and integrates domain priors with data-driven clustering to generate interpretable knowledge hierarchies. Evaluated on a newly introduced benchmark comprising over one million English–German multi-source documents, the method significantly improves cross-source consistency and hierarchy quality. Its practical utility is further demonstrated through the successful construction of regional innovation technology maps for Germany, highlighting its applicability in policy analysis and innovation monitoring.

Abstract

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.

Problem

• taxonomy induction
• heterogeneous corpora
• knowledge organization
• term-centric hierarchy
• cross-source alignment

Innovation

• term-centric
• hierarchy induction
• heterogeneous corpora
• cross-source alignment
• taxonomy learning