SciKnowOrg/ontolearner-scholarly_knowledge

Scholarly Knowledge Domain Ontologies

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:

pip install ontolearner

How to load an ontology or LLM4OL Paradigm tasks datasets?

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?

# 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

Citation

If you find our work helpful, feel free to give us a cite.

@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}
}