|
|
|
|
|
--- |
|
|
license: mit |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- OntoLearner |
|
|
- ontology-learning |
|
|
- scholarly-knowledge |
|
|
pretty_name: Scholarly Knowledge |
|
|
--- |
|
|
|
|
|
<div align="center"> |
|
|
<img src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png" alt="OntoLearner" |
|
|
style="display: block; margin: 0 auto; width: 500px; height: auto;"> |
|
|
<h1 style="text-align: center; margin-top: 1em;">Scholarly Knowledge Domain Ontologies</h1> |
|
|
<a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a> |
|
|
</div> |
|
|
|
|
|
## Overview |
|
|
The scholarly knowledge domain encompasses ontologies that systematically represent the intricate structures, processes, and governance mechanisms inherent in scholarly research, academic publications, and the supporting infrastructure. This domain is pivotal in facilitating the organization, retrieval, and dissemination of academic knowledge, thereby enhancing the efficiency and transparency of scholarly communication. By providing a formalized framework for knowledge representation, it supports interoperability and integration across diverse research disciplines and platforms. |
|
|
|
|
|
## Ontologies |
|
|
|
|
|
| Ontology ID | Full Name | Classes | Properties | Individuals | |
|
|
|-------------|-----------|---------|------------|-------------| |
|
|
| CSO | Computer Science Ontology (CSO) | 0 | 0 | 0| |
|
|
| OPMW | Open Provenance Model for Workflows (OPMW) | 59 | 87 | 2| |
|
|
| OBOE | Extensible Observation Ontology (OBOE) | 478 | 30 | 0| |
|
|
| SWO | Software Ontology (SWO) | 2746 | 165 | 443| |
|
|
| SEPIO | Scientific Evidence and Provenance Information Ontology (SEPIO) | 129 | 117 | 21| |
|
|
| LexInfo | LexInfo (LexInfo) | 334 | 189 | 276| |
|
|
| EXPO | Ontology of Scientific Experiments (EXPO) | 347 | 78 | 0| |
|
|
| SPDocument | SMART Protocols Ontology: Document Module (SP-Document) | 400 | 43 | 45| |
|
|
| SPWorkflow | SMART Protocols Ontology: Workflow Module (SP-Workflow) | 419 | 17 | 5| |
|
|
| NFDIcore | National Research Data Infrastructure Ontology (NFDIcore) | 302 | 102 | 0| |
|
|
| TribAIn | Tribology and Artificial Intelligence Ontology (TribAIn) | 241 | 64 | 21| |
|
|
| DCAT | Data Catalog Vocabulary (DCAT) | 10 | 39 | 0| |
|
|
| EURIO | EUropean Research Information Ontology (EURIO) | 44 | 111 | 0| |
|
|
| Metadata4Ing | Metadata for Intelligent Engineering (Metadata4Ing) | 48 | 100 | 47| |
|
|
| FRAPO | Funding, Research Administration and Projects Ontology (FRAPO) | 97 | 125 | 25| |
|
|
| FRBRoo | Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) | 83 | 0 | 0| |
|
|
| DUO | Data Use Ontology (DUO) | 45 | 1 | 0| |
|
|
| DataCite | DataCite Ontology (DataCite) | 19 | 10 | 70| |
|
|
| Framester | Framester Ontology (Framester) | 59 | 77 | 0| |
|
|
| CiTO | Citation Typing Ontology (CiTO) | 10 | 101 | 0| |
|
|
| VOAF | Vocabulary of a Friend (VOAF) | 3 | 21 | 1| |
|
|
| AIISO | Academic Institution Internal Structure Ontology (AIISO) | 22 | 0 | 0| |
|
|
| PreMOn | Pre-Modern Ontology (PreMOn) | 15 | 16 | 0| |
|
|
| PPlan | Ontology for Provenance and Plans (P-Plan) | 11 | 14 | 0| |
|
|
| WiLD | Workflows in Linked Data (WiLD) | 16 | 0 | 4| |
|
|
|
|
|
## Dataset Files |
|
|
Each ontology directory contains the following files: |
|
|
1. `<ontology_id>.<format>` - The original ontology file |
|
|
2. `term_typings.json` - Dataset of term to type mappings |
|
|
3. `taxonomies.json` - Dataset of taxonomic relations |
|
|
4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations |
|
|
5. `<ontology_id>.rst` - Documentation describing the ontology |
|
|
|
|
|
## Usage |
|
|
These datasets are intended for ontology learning research and applications. Here's how to use them with OntoLearner: |
|
|
|
|
|
First of all, install the `OntoLearner` library via PiP: |
|
|
|
|
|
```bash |
|
|
pip install ontolearner |
|
|
``` |
|
|
|
|
|
**How to load an ontology or LLM4OL Paradigm tasks datasets?** |
|
|
``` python |
|
|
from ontolearner import LexInfo |
|
|
|
|
|
ontology = LexInfo() |
|
|
|
|
|
# Load an ontology. |
|
|
ontology.load() |
|
|
|
|
|
# Load (or extract) LLMs4OL Paradigm tasks datasets |
|
|
data = ontology.extract() |
|
|
``` |
|
|
|
|
|
**How use the loaded dataset for LLM4OL Paradigm task settings?** |
|
|
``` python |
|
|
# Import core modules from the OntoLearner library |
|
|
from ontolearner import LexInfo, LearnerPipeline, train_test_split |
|
|
|
|
|
# Load the LexInfo ontology, which contains concepts related to wines, their properties, and categories |
|
|
ontology = LexInfo() |
|
|
ontology.load() # Load entities, types, and structured term annotations from the ontology |
|
|
data = ontology.extract() |
|
|
|
|
|
# Split into train and test sets |
|
|
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42) |
|
|
|
|
|
# Initialize a multi-component learning pipeline (retriever + LLM) |
|
|
# This configuration enables a Retrieval-Augmented Generation (RAG) setup |
|
|
pipeline = LearnerPipeline( |
|
|
retriever_id='sentence-transformers/all-MiniLM-L6-v2', # Dense retriever model for nearest neighbor search |
|
|
llm_id='Qwen/Qwen2.5-0.5B-Instruct', # Lightweight instruction-tuned LLM for reasoning |
|
|
hf_token='...', # Hugging Face token for accessing gated models |
|
|
batch_size=32, # Batch size for training/prediction if supported |
|
|
top_k=5 # Number of top retrievals to include in RAG prompting |
|
|
) |
|
|
|
|
|
# Run the pipeline: training, prediction, and evaluation in one call |
|
|
outputs = pipeline( |
|
|
train_data=train_data, |
|
|
test_data=test_data, |
|
|
evaluate=True, # Compute metrics like precision, recall, and F1 |
|
|
task='term-typing' # Specifies the task |
|
|
# Other options: "taxonomy-discovery" or "non-taxonomy-discovery" |
|
|
) |
|
|
|
|
|
# Print final evaluation metrics |
|
|
print("Metrics:", outputs['metrics']) |
|
|
|
|
|
# Print the total time taken for the full pipeline execution |
|
|
print("Elapsed time:", outputs['elapsed_time']) |
|
|
|
|
|
# Print all outputs (including predictions) |
|
|
print(outputs) |
|
|
``` |
|
|
|
|
|
For more detailed documentation, see the [](https://ontolearner.readthedocs.io) |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you find our work helpful, feel free to give us a cite. |
|
|
|
|
|
|
|
|
```bibtex |
|
|
@inproceedings{babaei2023llms4ol, |
|
|
title={LLMs4OL: Large language models for ontology learning}, |
|
|
author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{\"o}ren}, |
|
|
booktitle={International Semantic Web Conference}, |
|
|
pages={408--427}, |
|
|
year={2023}, |
|
|
organization={Springer} |
|
|
} |
|
|
``` |
|
|
|
