# AutoRDF2GML
## Overview
AutoRDF2GML is a framework designed to transform RDF data into graph representations suitable for graph-based machine learning methods, e.g., Graph Neural Networks (GNNs). It uniquely generates content-based features from RDF datatype properties and topology-based features from RDF object properties, enabling the effective integration of Semantic Web technologies with Graph Machine Learning.
## Installation
To install the current PyPI version, run:
```sh
pip install autordf2gml
```
We recommend users to use **isolated environment, such as venv or conda**, to use the library. Please note that the current version has only been tested with **Python versions 3.8 to 3.9.9**.
## Usage
To start using AutoRDF2GML, you need: **(1) RDF file** and **(2) Configuration file** describing the configuration for the transformation. In the configuration file, define the RDF classes and properties as needed for your project. See the following for quick example.
## Quick Example
This example uses the [semopenalex-C1793878-sample.nt](https://github.com/davidlamprecht/AutoRDF2GML/blob/main/example/semopenalex-C1793878-sample.nt) RDF file, a curated subset from [SemOpenAlex](https://semopenalex.org).
#### 1. Preparing the configuration file
Fill all the required fields in the config file: see [config-soa-cb.ini](https://github.com/davidlamprecht/AutoRDF2GML/blob/main/example/config-soa-cb.ini) and [config-soa-tb.ini](https://github.com/davidlamprecht/AutoRDF2GML/blob/main/example/example-topologyfeatures/config-soa-tb.ini) as examples for the content-based and topology-based transformation, respectively. The following shows an example of the config file format:
```ini
[InputPath] ;required
input_path = semopenalex-C1793878-sample.nt
[SavePath] ;required
save_path_numeric_graph = semopenalex/numeric-graph/
save_path_mapping = semopenalex/mapping/
[NLD] ;required
nld_class = work
[EMBEDDING] ;required
embedding_model = allenai/scibert_scivocab_uncased
[Nodes] ;required
classes = work, author, institution, source, concept, publisher
work = https://semopenalex.org/class/Work
author = https://semopenalex.org/class/Author
institution = https://semopenalex.org/class/Institution
[SimpleEdges] ;required
edge_names = author_institution
author_institution_start_node = author
author_institution_properties = http://www.w3.org/ns/org#memberOf
author_institution_end_node = institution
```
#### 2. Using the library
```python
import autordf2gml
#to run content-based transformation
autordf2gml.content_feature("config-soa-cb.ini")
#to run topology-based transformation
autordf2gml.topology_feature("config-soa-tb.ini")
#to run content-based transformation only using simple-edges
autordf2gml.simpleedges_feature("config-aifb-cb-simple.ini")
```
## Our Github
The most recent updates, documentation, and examples can be accessed through the following repository:
- <https://github.com/davidlamprecht/AutoRDF2GML>
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"description": "# AutoRDF2GML\n\n## Overview\n\nAutoRDF2GML is a framework designed to transform RDF data into graph representations suitable for graph-based machine learning methods, e.g., Graph Neural Networks (GNNs). It uniquely generates content-based features from RDF datatype properties and topology-based features from RDF object properties, enabling the effective integration of Semantic Web technologies with Graph Machine Learning.\n\n## Installation\n\nTo install the current PyPI version, run:\n\n```sh\npip install autordf2gml\n```\n\nWe recommend users to use **isolated environment, such as venv or conda**, to use the library. Please note that the current version has only been tested with **Python versions 3.8 to 3.9.9**. \n\n## Usage\n\nTo start using AutoRDF2GML, you need: **(1) RDF file** and **(2) Configuration file** describing the configuration for the transformation. In the configuration file, define the RDF classes and properties as needed for your project. See the following for quick example. \n\n## Quick Example\n\nThis example uses the [semopenalex-C1793878-sample.nt](https://github.com/davidlamprecht/AutoRDF2GML/blob/main/example/semopenalex-C1793878-sample.nt) RDF file, a curated subset from [SemOpenAlex](https://semopenalex.org).\n\n#### 1. Preparing the configuration file\n\nFill all the required fields in the config file: see [config-soa-cb.ini](https://github.com/davidlamprecht/AutoRDF2GML/blob/main/example/config-soa-cb.ini) and [config-soa-tb.ini](https://github.com/davidlamprecht/AutoRDF2GML/blob/main/example/example-topologyfeatures/config-soa-tb.ini) as examples for the content-based and topology-based transformation, respectively. The following shows an example of the config file format:\n\n ```ini\n [InputPath] ;required\n input_path = semopenalex-C1793878-sample.nt\n\n [SavePath] ;required\n save_path_numeric_graph = semopenalex/numeric-graph/\n save_path_mapping = semopenalex/mapping/\n\n [NLD] ;required\n nld_class = work\n\n [EMBEDDING] ;required\n embedding_model = allenai/scibert_scivocab_uncased\n\n [Nodes] ;required\n classes = work, author, institution, source, concept, publisher\n work = https://semopenalex.org/class/Work\n author = https://semopenalex.org/class/Author\n institution = https://semopenalex.org/class/Institution\n \n [SimpleEdges] ;required\n edge_names = author_institution\n author_institution_start_node = author\n author_institution_properties = http://www.w3.org/ns/org#memberOf\n author_institution_end_node = institution\n ```\n\n#### 2. Using the library\n\n```python\nimport autordf2gml\n\n#to run content-based transformation\nautordf2gml.content_feature(\"config-soa-cb.ini\") \n\n#to run topology-based transformation\nautordf2gml.topology_feature(\"config-soa-tb.ini\") \n\n#to run content-based transformation only using simple-edges\nautordf2gml.simpleedges_feature(\"config-aifb-cb-simple.ini\")\n```\n\n## Our Github\n\nThe most recent updates, documentation, and examples can be accessed through the following repository:\n\n- <https://github.com/davidlamprecht/AutoRDF2GML>\n",
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