Nanopublications

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@prefix dct: <http://purl.org/dc/terms/> .
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@prefix prov: <http://www.w3.org/ns/prov#> .
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sub:assertion {
  <https://doi.org/10.48550/arXiv.2508.21491> dct:title "Geospatial Qestion Answering on Historical Maps Using Spatio-Temporal Knowledge Graphs and Large Language Models" ;
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#SpatioTemporalGeoQASystem> ;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#LangChainLLMAgent> ;
    a prov:Entity .
  <https://neverblink.eu/ontologies/llm-kg/methods#LangChainLLMAgent> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "LangChain LLM agent" .
  <https://neverblink.eu/ontologies/llm-kg/methods#SpatioTemporalGeoQASystem> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGQuestionAnswering> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method describes a comprehensive GeoQA system that integrates a constructed spatio-temporal knowledge graph (KG) with large language models (LLMs). LLMs are utilized to convert natural language questions into SPARQL queries, perform query validation, decompose complex descriptive questions, and generate natural language answers by reasoning over KG-retrieved facts and additional contextual information. The core goal is to enhance question answering capabilities over historical geospatial KGs." ;
    rdfs:label "Spatio-Temporal GeoQA System" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG> .
}

sub:provenance {
sub:assertion prov:wasAttributedTo <https://neverblink.eu/ontologies/llm-kg/agent> ;
prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2508.21491> .
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