Mapping Political-Elite Networks in Europe with a Multilingual Joint Entity-Relation Extraction Pipeline

2026-06-25

AI Summary

Existing approaches struggle to capture at scale the complex, informal, and adversarial relationship networks among European political elites, constrained by manual annotation or insufficient cross-lingual capabilities. This work proposes the first end-to-end, scalable multilingual joint entity–relation extraction pipeline, integrating span-based named entity recognition, a three-stage Wikidata entity linking module, ontology-constrained relation extraction, and guided decoding to automatically construct signed, timestamped knowledge graphs from massive news corpora. The framework enables language-agnostic entity alignment and directed relation extraction, achieving 68.2% (strict) to 93.7% (lenient) accuracy on a gold-standard set of 3,491 relations. It successfully reconstructs Austrian party system evolution and uncovers the structure of Polish political–business networks.

Abstract

Whether political elites organise into rent-seeking coalitions that capture public resources or civic networks that sustain governance is a central question in comparative politics. Yet observing these complex, informal, and adversarial ties at scale has historically required intensive manual coding, while automated text-as-data methods have largely been limited to simple co-occurrence. Recent large language model (LLM) approaches offer a path forward but often rely on proprietary APIs, lack cross-lingual capability, and struggle with scalable entity resolution. We present a modular, fully open-weight pipeline for multilingual joint entity-relation extraction that builds signed, temporal knowledge graphs from massive unstructured news corpora. It combines span-based named-entity recognition (NER) with a three-stage linking cascade mapping mentions to language-independent Wikidata identifiers; a high-throughput, ontology-constrained mixture-of-experts model then uses guided decoding to extract directed, signed relationships grounded in a domain ontology. A full-coverage spot-check against a 3491-relation gold standard shows high textual correctness (68.2% strict to 93.7% lenient). Two large-scale case studies validate the pipeline against the public record. In Austria, it reconstructs a political party's complete lifecycle, dating internal fractures and tracking personnel into successor factions and court convictions. In a Polish corpus, it uncovers the overlapping economic and governance networks of state-enterprise patronage, alongside the structurally balanced, signed conflict network of the polarized Civic Platform (Platforma Obywatelska, PO)--Law and Justice (Prawo i Sprawiedliwość, PiS) duopoly. By bridging raw multilingual text and structured relational data, our framework provides a robust, replicable foundation for cross-national empirical computational social science.

Problem

• political-elite networks
• multilingual entity-relation extraction
• informal ties
• cross-lingual NLP
• computational social science

Innovation

• multilingual joint entity-relation extraction
• signed temporal knowledge graph
• Wikidata-based entity linking
• ontology-constrained mixture-of-experts
• guided decoding