TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action
Year: 2026
Type: conference-paper
Abstract: Humanitarian Mine Action (HMA) addresses the challenge of detecting and removing landmines from conflict regions.Much of the life-saving operational knowledge produced by HMA agencies is buried in unstructured reports, limiting the transferability of information between agencies.To address this issue, we propose TextMineX: the first dataset, evaluation framework and ontology-guided large language model (LLM) pipeline for knowledge extraction from text in the HMA domain.TextMineX structures HMA reports into (subject, relation, object)-triples, thus creating domain-specific knowledge.To ensure real-world relevance, we utilized the dataset from our collaborator Cambodian Mine Action Centre (CMAC).We further introduce a bias-aware evaluation framework that combines human-annotated triples with an LLM-as-Judge protocol to mitigate position bias in reference-free scoring.Our experiments show that ontology-aligned prompts improve extraction accuracy by up to 44.2%, reduce hallucinations by 22.5%, and enhance format adherence by 20.9% compared to baseline models.We publicly release the dataset and code 1 .
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140114255