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Semantic Perspectives on the Lake District Writing: Spatial Ontology Modeling and Relation Extraction for Deeper Insights

Authors Erum Haris , Anthony G. Cohn , John G. Stell

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Author Details

Erum Haris
  • School of Computing, University of Leeds, UK
Anthony G. Cohn
  • School of Computing, University of Leeds, UK
  • The Alan Turing Institute, London, UK
John G. Stell
  • School of Computing, University of Leeds, UK

Funding

The research is supported by the Economic and Social Research Council (ESRC) under grant ES/W003473/1.

Acknowledgements

The support of Microsoft under their Accelerating Foundation Models Research initiative in providing Azure credits to AGC is also acknowledged. Finally, we thank the entire team of the Spatial Narratives project for their discussions on the CLDW and this work.

Cite AsGet BibTex

Erum Haris, Anthony G. Cohn, and John G. Stell. Semantic Perspectives on the Lake District Writing: Spatial Ontology Modeling and Relation Extraction for Deeper Insights. In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.COSIT.2024.11

Abstract

Extracting spatial details from historical texts can be difficult, hindering our understanding of past landscapes. The study addresses this challenge by analyzing the Corpus of the Lake District Writing, focusing on the English Lake District region. We systematically link the theoretical notions from the core concepts of spatial information to provide basis for the problem domain. The conceptual foundation is further complemented with a spatial ontology and a custom gazetteer, allowing a formal and insightful semantic exploration of the massive unstructured corpus. The other contrasting side of the framework is the usage of LLMs for spatial relation extraction. We formulate prompts leveraging understanding of the LLMs of the intended task, curate a list of spatial relations representing the most recurring proximity or vicinity relations terms and extract semantic triples for the top five place names appearing in the corpus. We compare the extraction capabilities of three benchmark LLMs for a scholarly significant historical archive, representing their potential in a challenging and interdisciplinary research problem. Finally, the network comprising the semantic triples is enhanced by incorporating a gazetteer-based classification of the objects involved thus improving their spatial profiling.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
Keywords
  • spatial humanities
  • spatial narratives
  • ontology
  • large language models

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