CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts

2026-02-19

AI Summary

This work addresses the challenge of relation extraction between persons and locations in noisy, multilingual historical texts by proposing a fine-grained relation classification method that integrates spatiotemporal cues to distinguish between “visited” (at) and “located-at-publication-time” (isAt) semantic relations. It introduces the first evaluation framework for historical relation extraction that jointly assesses accuracy, computational efficiency, and cross-period generalization, leveraging multilingual NLP models combined with an efficient spatiotemporal reasoning mechanism. The resulting approach provides a robust tool for large-scale processing of historical documents, effectively supporting knowledge graph construction and biographical reconstruction in digital humanities research.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person--place associations in multiple languages and time periods. Systems are asked to classify relations of two types - $at$ ("Has the person ever been at this place?") and $isAt$ ("Is the person located at this place around publication time?") - requiring reasoning over temporal and geographical cues. The lab introduces a three-fold evaluation profile that jointly assesses accuracy, computational efficiency, and domain generalization. By linking relation extraction to large-scale historical data processing, HIPE-2026 aims to support downstream applications in knowledge-graph construction, historical biography reconstruction, and spatial analysis in digital humanities.

Problem

• person-place relation extraction
• multilingual historical texts
• temporal reasoning
• geographical reasoning
• digital humanities

Innovation

• person-place relation extraction
• multilingual historical texts
• temporal reasoning
• computational efficiency
• domain generalization