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Datasets:
SciKnowOrg
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ontolearner-scholarly_knowledge

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Tags:
OntoLearner
ontology-learning
scholarly_knowledge
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    AttributeError
Message:      'str' object has no attribute 'items'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 165, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1664, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1621, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1068, in get_module
                  {
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1069, in <dictcomp>
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
              AttributeError: 'str' object has no attribute 'items'

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OntoLearner

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.

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:

from ontolearner import LearnerPipeline, AutoLearnerLLM, Wine, train_test_split

# Load ontology (automatically downloads from Hugging Face)
ontology = Wine()
ontology.load()

# Extract the dataset
data = ontology.extract()

# Split into train and test sets
train_data, test_data = train_test_split(data, test_size=0.2)

# Create a learning pipeline (for RAG-based learning)
pipeline = LearnerPipeline(
    task="term-typing",  # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
    retriever_id="sentence-transformers/all-MiniLM-L6-v2",
    llm_id="mistralai/Mistral-7B-Instruct-v0.1",
    hf_token="your_huggingface_token"  # Only needed for gated models
)

# Train and evaluate
results, metrics = pipeline.fit_predict_evaluate(
    train_data=train_data,
    test_data=test_data,
    top_k=3,
    test_limit=10
)

For more detailed examples, see the OntoLearner documentation.

Citation

If you use these ontologies in your research, please cite:

@software{babaei_giglou_2025,
  author       = {Babaei Giglou, Hamed and D'Souza, Jennifer and Aioanei, Andrei and Mihindukulasooriya, Nandana and Auer, Sören},
  title        = {OntoLearner: A Modular Python Library for Ontology Learning with LLMs},
  month        = may,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v1.0.1},
  doi          = {10.5281/zenodo.15399783},
  url          = {https://doi.org/10.5281/zenodo.15399783},
}
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