What question did this study set out to answer?

This research aims to create an ontology and knowledge graph to aid in assessing risks and impacts in nature finance.

December 8, 2025Frontiers in Artificial IntelligenceOpen Access

NatureKG: an ontology and knowledge graph for nature finance with a Text2Cypher application

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Authors

ASAlok Kumar SinghPandit Deendayal Petroleum University

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Implication

Feasibility study demonstrates LLM-assisted querying for environmental risks and financial assets, suggesting improvements in nature finance analysis.

Key Points

This research aims to create an ontology and knowledge graph to aid in assessing risks and impacts in nature finance.
Developed a domain ontology based on ENCORE and peer-reviewed literature.
Instantiated NatureKG in Neo4j with 320 nodes and 540 relationships.
Constructed a Text2Cypher dataset; fine-tuned three large language models for natural language query translation.
Phi-3 model achieved the highest execution accuracy (0.21) and Macro F1 score (0.56).
LLaMA-3.1-8B exhibited balanced performance across tasks.
Results indicate small, fine-tuned models can be effective for low-resource, domain-specific querying.

Cite This Study

Alok Kumar Singh (2025) studied this question.

synapsesocial.com/papers/693624ba4fa91c937236c90d https://doi.org/10.3389/frai.2025.1693843

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Implication

Feasibility study demonstrates LLM-assisted querying for environmental risks and financial assets, suggesting improvements in nature finance analysis.

NatureKG: an ontology and knowledge graph for nature finance with a Text2Cypher application

Discussion

Member takes

Implication

Key Points

Cite This Study

NatureKG: an ontology and knowledge graph for nature finance with a Text2Cypher application

Discussion

Member takes

Implication

Key Points

Cite This Study

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