Graphusion: A RAG Framework for Scientific Knowledge Graph Construction with a Global Perspective
Rui Yang, Boming Yang, Xinjie Zhao, Fan Gao, Aosong Feng, Sixun Ouyang, Moritz Blum, Tianwei She, Yuang Jiang, Freddy Lecue, Jinghui Lu, Irene Li;
Accepted by NLP4KGC workshop, WWW 2025.
Graphusion is a pipeline that extracts Knowledge Graph triples from text.
Create a new conda environment and install the required packages:
conda create -n graphusion python=3.10
conda activate graphusion
pip install -r requirements.txt
Sensitive data like API and database keys shall be stored in a private_config.json file in the root directory.
The file should have the following structure:
{
"OPENAI_API_KEY": "[key]",
"GOOGLE_API_KEY": "[key]",
"NEO4J": {
"URI": "[uri]",
"USER": "[user]",
"PASSWORD":"[password]"
}
}
The pipeline processes text files from the data/[dataset_name]/raw directory (e.g., data/test/raw) as input.
Furthermore, the pipeline requires relation definitions as a JSON file. This file defines the relations and
provides a description of the relation (e.g., data/test/relation_types.json). In addition, some information can be
provided to improve the results (--gold_concept_file, --refined_concepts_file, --annotated_graph_file)
or to skip pipeline steps (--input_json_file, --input_triple_file). See parameters below.
The ACL data is originally in a csv format. Therefore, we provide the notebook preprocess.ipynb to convert the
data into the required text files.
The pipeline can be run using the following command:
usage: main.py [-h] [--run_name RUN_NAME] --dataset DATASET --relation_definitions_file RELATION_DEFINITIONS_FILE [--input_json_file INPUT_JSON_FILE]
[--input_triple_file INPUT_TRIPLE_FILE] [--model MODEL] [--max_resp_tok MAX_RESP_TOK] [--max_input_char MAX_INPUT_CHAR]
[--prompt_tpextraction PROMPT_TPEXTRACTION] [--prompt_fusion PROMPT_FUSION] [--gold_concept_file GOLD_CONCEPT_FILE]
[--refined_concepts_file REFINED_CONCEPTS_FILE] [--annotated_graph_file ANNOTATED_GRAPH_FILE] [--language LANGUAGE] [--verbose]
options:
-h, --help show this help message and exit
--run_name RUN_NAME Assign a name to this run. The name will be used to, e.g., determine the output directory. We recommend to use unique and descriptive names to
distinguish the results of different models.
--dataset DATASET Name of the dataset. Is used to, e.g., determine the input directory.
--relation_definitions_file RELATION_DEFINITIONS_FILE
Path to the relation definitions file. The file should be a JSON file, where the keys are the relation types and the values are dictionaries with the
following keys: 'label', 'description'.
--input_json_file INPUT_JSON_FILE
Path to the input file. Step 1 will be skipped if this argument is provided. The input file should be a JSON file with the following structure:
{'concept1': [{'abstract': ['abstract1', ...], 'label: 0},...} E.g. data/test/concept_abstracts.json is the associated file createddurin step 1 in the
test run.
--input_triple_file INPUT_TRIPLE_FILE
Path to the input file storing the triples in the format as outputted by the candidate triple extraction model. Step 1 and step 2 will be skipped if
this argument is provided.
--model MODEL Name of the LLM that should be used for the KG construction.
--max_resp_tok MAX_RESP_TOK
Maximum number of tokens in the response of the candidate triple extraction model.
--max_input_char MAX_INPUT_CHAR
Maximum number of characters in the input of the candidate triple extraction model.
--prompt_tpextraction PROMPT_TPEXTRACTION
Path to the prompt template for step 1.
--prompt_fusion PROMPT_FUSION
Path to the prompt template for fusion.
--gold_concept_file GOLD_CONCEPT_FILE
Path to a file with concepts that are provided by experts. The file should be a tsv file, each row should look like: 'concept id | concept
--refined_concepts_file REFINED_CONCEPTS_FILE
In step 2 (candidate triple extraction) many new concepts might be added. Instead of using these, concepts can be provided through this parameter.
Specify the path to a file with refined concepts of the graph. The file should be a tsv file, each row should look like: "concept id | concept name"
--annotated_graph_file ANNOTATED_GRAPH_FILE
Path to the annotated graph.
--language LANGUAGE Language of the abstracts.
--verbose Print additional information to the console.
The output of the pipeline are the following files:
concept_abstracts: The json file mapping the extracted concepts to their abstracts.step-02.jsonl: The extracted triples in linewise JSON format.step-03.jsonl: The fused triples in linewise JSON format.
To run the full pipeline on a small sample (test) dataset, call:
python main.py --run_name "test" --dataset "test" --relation_definitions_file "data/test/relation_types.json" --gold_concept_file "data/test/gold_concepts.tsv" --refined_concepts_file "data/test/refined_concepts.tsv"
To reproduce the Graphusion results on the ACL (nlp) dataset, call: python main.py --run_name "acl" --dataset "nlp" --relation_definitions_file "data/nlp/relation_types.json" --gold_concept_file "data/nlp/gold_concepts.tsv" --refined_concepts_file "data/nlp/refined_concepts.tsv"`
This implementation is based on the code of Rui Yang and Irene Li. Moritz Blum extended their code and implemented this pipeline.
@inproceedings{yang2025graphusion,
title={Graphusion: A RAG Framework for Scientific Knowledge Graph Construction with a Global Perspective},
author={Yang, Rui and Yang, Boming and Zhao, Xinjie and Gao, Fan and Feng, Aosong and Ouyang, Sixun and Blum, Moritz and She, Tianwei and Jiang, Yuang and Lecue, Freddy and Lu, Jinghui and Li, Irene},
booktitle={Proceedings of the NLP4KGC Workshop at the World Wide Web Conference (WWW) 2025},
year={2025}
}