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

A large-scale dataset of sub-institution name disambiguation and hierarchical structures from OpenAlex

Accurate attribution of scholarly work to specific sub-institutional units, such as schools or departments of a university, is crucial for granular research assessment and policymaking. While robust identifiers exist for top-level institutions, standardized data for sub-level units remains scarce due to the linguistic and structural variability of affiliation strings. In this study, we introduce OpenSubAffil, a large-scale dataset mapping raw affiliation strings from OpenAlex to disambiguated sub-institutional entities and their hierarchical structures. We developed a pipeline integrating named entity recognition (NER) with embedding-based clustering. Furthermore, we proposed a multi-signal scoring function that synthesizes lexical and co-occurrence evidence to reconstruct the sub-institutional hierarchy. OpenSubAffil comprises mappings for 40 million affiliation strings to 638,843 disambiguated sub-units across 18,635 top-level institutions, together with their hierarchical relationships. Validation against Wikidata benchmarks and manual investigation show that our method achieves promising performance. Overall, this dataset bridges the granularity gap between individual researchers and top-level institutions, enabling high-resolution analyses of scholarly output and communication at the sub-institutional level. The OpenSubAffil dataset is publicly available at https://doi.org/10.5281/zenodo.19602782.

Sophocles’ Antigone as a Knowledge Graph through a Hybrid Collaborative Workflow with Ontology-Guided LLM Extraction

A unified transformer-BiLSTM and graph attention network framework for explainable multilingual opinion mining and relationship inference in complex social-citation networks

An innovative model which combines transformer-based context embedding, BiLSTM for capturing of sentiment flows, and GAT for examining relational data is introduced which incorporates contextual, sequential and relational modelling of multilingual opinion mining.

Linking on the Topology of Entity Co-occurrence Networks for Social Media Analysis

Lightweight Person-Place Relation Extraction from Historical Newspapers with Dependency Graphs and Proximity Features

This team investigates how far a lightweight, interpretable system can go without any pretrained language model at the relation classification stage on the HIPE-2026 shared task, finding that minimum character distance alone captures most of the classification signal.

Conceptual Networks for Cross-Linguistic Idiomatic Expressions: A Feature-Based Graph Approach

We present an interpretable network-based framework for representing idiomatic and figurative meaning across eight typologically diverse languages, totaling 160 conventional expressions, the large majority of which are idiomatic. Each expression is annotated with binary conceptual features (containment, concealment, emotional, social, etc.) derived from cognitive-linguistic theory, and pairwise Jaccard similarities define a weighted graph. Community detection reveals that idioms cluster by conceptual schema rather than by language, producing a structure consistent with cognitive-linguistic predictions. The conceptual network captures unique semantic information not present in distributional embeddings, can be scaled via automatic annotation with LLMs, improves downstream idiom detection, and remains robust when enriched with corpus frequencies. Cross-lingual transfer experiments show that conceptual proximity alone can identify acceptable translation equivalents across five language families, with substantial gains over embedding-based baselines. Ablation studies demonstrate that all three feature dimensions -- schemas, roles, and valence -- contribute non-redundantly to both the network's organizational properties and its performance on idiom detection, and that specific graph-derived signals (community membership, neighbor similarity) are particularly informative. The framework offers an interpretable, cross-linguistically stable representation of idiomatic meaning, combining theoretical grounding with practical utility.