BSVGK/Text_to_KG_Construction_Dataset

Text-to-KG Construction Dataset (UK Government Contracts)

Dataset Summary

This dataset contains 9,244 verified UK government procurement contracts paired with structured RDF knowledge graph triples, constructed for the task of automated Text-to-KG extraction. It was developed as part of a UEL–Depixen industrial placement research project focused on building trustworthy, hallucination-free domain-specific SLMs.

This dataset was used to fine-tune:

• 👉 BSVGK/phi35-mini-lora-text2kg-merged — Zero hallucination across 1,387 unseen contracts
• 👉 BSVGK/phi35-mini-lora-text2kg-adapter — LoRA adapter

Dataset Details

Property	Value
Domain	UK Government Procurement Contracts
Total Samples	9,244 training + 1,387 test
Format	Contract text → RDF Triples
Language	English
Source	UK Government procurement data
License	MIT

Dataset Structure

Each sample contains:

{ "input": "Raw UK government contract text...", "output": [ {"subject": "entity_1", "predicate": "relation", "object": "entity_2"}, {"subject": "entity_1", "predicate": "relation", "object": "entity_3"} ] }

Construction Process

1. Data Collection — UK government procurement contracts collected from public sources
2. Preprocessing — Cleaning, deduplication, and normalisation of contract text
3. Triple Extraction — Manual and automated RDF triple annotation
4. Verification — Each triple verified against source contract text
5. Quality Control — Dual-level hallucination check (L1: relation validity, L2: entity grounding)

Hallucination Evaluation Framework

This dataset was evaluated using a novel dual-level hallucination framework:

• L1 — Relation Validity: All relations verified against a predefined ontology
• L2 — Entity Grounding: All entities grounded in the source contract text

This ensured zero hallucination in the fine-tuned Phi-3.5 model across 1,387 unseen test contracts.

Models Trained on This Dataset

Model	F1	BERTScore	Hallucination Rate
Phi-3.5 Mini Instruct (LoRA)	0.9954	0.9997	0.00%
Gemma 2 2B IT (QLoRA)	competitive	competitive	higher

Intended Use

• Training SLMs for knowledge graph construction
• Research in trustworthy and hallucination-free NLP
• Information extraction from legal and procurement documents
• RDF triple generation for semantic web applications

Out of Scope

• Non-English contracts
• Contracts outside UK government procurement domain
• General purpose NLP tasks

Citation

@misc{bubathula2026texttokg_dataset, author = {Sai Venkata Gopala Krishna Bubathula}, title = {Text-to-KG Construction Dataset: UK Government Procurement Contracts for RDF Triple Extraction}, year = {2026}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/BSVGK/Text_to_KG_Construction_Dataset}, institution = {University of East London & Depixen} }

Developer

Sai Venkata Gopala Krishna Bubathula

• 🎓 MSc Big Data Technologies, University of East London
• 🏢 AI Engineer — UEL–Depixen Industrial Placement
• 🔗 GitHub
• 🔗 LinkedIn
• 🔗 HuggingFace