Unstructured text, such as narratives describing the movement of people, can reveal valuable spatial information that is used to generate the routes people take. However, the lack of precision and the ambiguity of spatial information in these narratives create a significant problem in generating such routes. Existing work uses either traditional natural language processing (NLP) techniques or more recent large language models (LLMs) to extract relevant spatial information. However, traditional NLP techniques do not capture the contextual information in the text, and LLMs are often trained on data with insufficient coverage of developing countries, resulting in incomplete spatial information. This paper proposes a novel neuro-symbolic GeoAI framework called Narratives as Geographical Routes (NaR) to automatically extract and visualize geospatial routes from unstructured text and resolve spatial data quality issues in these texts. NaR extracts geographical information from narratives, identifies the toponyms, lists them in temporal order, resolves possible ambiguities, assigns their precise coordinates, and finally depicts the spatial routes on a map. This is achieved through the use of (1) retrieval augmented generation (RAG) techniques that leverage the geographical domain knowledge extracted from NLP techniques in conjunction with a gazetteer to improve the results of LLMs for toponym identification and temporal listing, and (2) a neuro-symbolic framework that uses symbolic reasoning to resolve toponym ambiguity. Experimental evaluation of our framework indicates that NaR outperforms other existing methods.
Karabatis et al. (Tue,) studied this question.