---
license: odc-by
language:
- en
pretty_name: Local GraphRAG Research Artifacts
task_categories:
- question-answering
- text-retrieval
tags:
- graphrag
- knowledge-graphs
- entity-resolution
- relation-recovery
- multi-hop-reasoning
configs:
- config_name: questions
  default: true
  data_files: "corpus/questions.jsonl"
- config_name: documents
  data_files: "corpus/documents.jsonl"
- config_name: extraction_calls
  data_files: "extraction/*/raw_calls.jsonl"
- config_name: extraction_chunks
  data_files: "extraction/*/normalized_chunks.jsonl"
- config_name: extraction_entities
  data_files: "extraction/*/normalized_entities.jsonl"
- config_name: extraction_relations
  data_files: "extraction/*/normalized_relations.jsonl"
- config_name: graph_nodes
  data_files: "graphs/*/*/nodes.jsonl"
- config_name: graph_edges
  data_files: "graphs/*/*/edges.jsonl"
- config_name: retrieval
  data_files: "retrieval/*/*.jsonl"
- config_name: answers
  data_files: "answers/*/*.jsonl"
- config_name: question_metrics
  data_files: "evaluation/question_metrics/*/*.jsonl"
- config_name: paired_comparisons
  data_files: "evaluation/builders/*/paired_*.jsonl"
---

# Local GraphRAG Research Artifacts

This repository contains the data and output artifacts produced for my master's
thesis research on knowledge graph construction in local GraphRAG systems. It
is an evidence package for the study, rather than the code used to run the
experiment.

The collection includes the study corpus, model extraction outputs, final
knowledge graphs, retrieved evidence, generated answers, and evaluation results.
Together, these files make it possible to inspect the experiment from source
documents through to question-level outcomes.

The complete experiment harness, including the code, configuration, and commands
used to produce these artifacts, is available on [GitHub](https://github.com/boblaros/local-graph-rag).

## What was compared

The experiment used 12 local language models to build graphs under three
cumulative conditions:

```text
Native -> ER -> ER+RR
```

`Native` is the original LightRAG graph. `ER` applies Entity Resolution to that
graph. `ER+RR` then applies Relation Recovery to the ER result. Because the
conditions form a sequence, this collection does not include a separate
Native+RR condition or measure RR as an independent intervention.

## What is included

- 120 MultiHop-RAG questions: 30 inference, 30 comparison, 30 temporal, and 30
  unanswerable.
- 155 documents: 125 gold documents and 30 hard negatives.
- 12 builder models and 36 graph conditions.
- Raw model calls and normalized extraction records.
- Final graph nodes and edges for every model and condition.
- 36 retrieval outputs, 36 generated-answer outputs, and 4,320 question-level
  evaluation records.
- Paired comparisons between the cumulative conditions and exploratory
  cross-model analyses.

<details>
<summary>Builder model identifiers</summary>

`gemma3_270m`, `gemma3_1b`, `gemma3_4b`, `gemma3_12b`, `qwen3_0_6b`,
`qwen3_1_7b`, `qwen3_4b`, `qwen3_8b`, `qwen35_0_8b`, `qwen35_2b`,
`qwen35_4b`, and `qwen35_9b`.

</details>

## Finding the files

| Directory | Contents |
| --- | --- |
| `corpus/` | Questions, source documents, and subset provenance |
| `extraction/` | Raw model calls and normalized chunks, entities, and relations |
| `graphs/` | Final graph nodes and edges |
| `retrieval/` | Evidence retrieved for each question |
| `answers/` | Answers generated from the retrieved evidence |
| `evaluation/` | Question-level metrics, paired comparisons, and aggregate results |

Files are grouped first by builder model and then, where applicable, by graph
condition. The top-level `manifest.json` provides the role, record count, source,
size, and checksum of every published payload file.

## Loading the data

Each JSONL file has one consistent schema and can be loaded separately with
🤗 Datasets. For example:

```python
from datasets import load_dataset

questions = load_dataset(
    "json",
    data_files="corpus/questions.jsonl",
    split="train",
)

metrics = load_dataset(
    "json",
    data_files="evaluation/question_metrics/qwen35_2b/native_lightrag.jsonl",
    split="train",
)
```

The different artifact families use different schemas. Question-level files can
be joined on `question_id`. Model, run, and condition fields preserve lineage
between extraction, graphs, retrieval, answers, and evaluation.

## Integrity

On Linux:

```bash
sha256sum -c checksums.sha256
```

On macOS:

```bash
shasum -a 256 -c checksums.sha256
```

## Study design and provenance

The corpus is a fixed, question-type-balanced subset derived from MultiHop-RAG.
Extraction was performed once per builder, after which ER and RR reused the
saved extraction records. Retrieval and answering used the same questions and
fixed downstream models in all three conditions. The records and manifest
preserve model identities, hashes, schema versions, and run lineage.

Mutable LightRAG workspaces, vector stores, logs, caches, and model weights are
not included because they are execution infrastructure rather than the research
artifacts intended for analysis and reuse. See the experiment harness linked
above for the implementation and reproduction workflow.

## Limitations

The subset is balanced by question type rather than sampled to reproduce the
full source distribution. Model outputs may contain extraction, retrieval, or
answering errors and should not be treated as verified facts. Graph structure
alone does not establish semantic correctness or downstream answer quality.
The corpus contains public news text and may mention identifiable people and
organizations present in the source articles.

## License

The source MultiHop-RAG data and this derived artifact collection are provided
under the Open Data Commons Attribution License (ODC-By) v1.0. Attribute Yixuan
Tang and Yi Yang and retain the license and provenance notices when redistributing
the data. Generated artifacts also retain model and source lineage for research
attribution.

ODC-By governs the database and attribution terms for this package. Underlying
article content may remain subject to rights held by its original publishers;
users should review the relevant source terms for uses beyond research and
reproducibility.

## Citation

Please cite the accompanying thesis:

```bibtex
@mastersthesis{kutivadze2026graphrag,
  author = {Georgii Kutivadze},
  title = {Knowledge Graph Construction Quality in Local GraphRAG: An Empirical Study of Model Capacity, Graph Post-Processing, and Downstream Performance},
  school = {Università Cattolica del Sacro Cuore},
  year = {2026},
  type = {Master's thesis}
}
```

Please also cite the source dataset and graph framework:

```bibtex
@misc{tang2024multihoprag,
  title = {MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries},
  author = {Yixuan Tang and Yi Yang},
  year = {2024},
  eprint = {2401.15391},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL}
}

@article{guo2024lightrag,
  title = {LightRAG: Simple and Fast Retrieval-Augmented Generation},
  author = {Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
  year = {2024},
  eprint = {2410.05779},
  archivePrefix = {arXiv},
  primaryClass = {cs.IR}
}
```