https://github.com/astrazeneca/onto_merger
OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes that represent the same domain.
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes that represent the same domain.
Basic Info
Statistics
- Stars: 98
- Watchers: 6
- Forks: 6
- Open Issues: 5
- Releases: 0
Topics
Metadata Files
README.md
Paper | Documentation | External Resources
OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes,(i.e. ontology concepts) that represent the same domain, e.g. diseases, and connecting them to form a single directed acyclic hierarchical graph (DAG) (i.e. an ontology class hierarchy). The library implements a pipeline that takes nodes, mappings and (disconnected) hierarchies as input and produces node merges and a connected hierarchy. It also provides analysis and data testing for fine tuning the inputs in order to further reduce duplication, as well as to increase connectivity.
Citing
If you find OntoMerger useful in your work or research, please consider adding the following citation:
```bibtex @misc{ontomerger, doi = {10.48550/ARXIV.2206.02238}, author = {Geleta, David and Nikolov, Andriy and ODonoghue, Mark and Rozemberczki, Benedek and Gogleva, Anna and Tamma, Valentina and Payne, Terry R.}, title = {OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes}, publisher = {arXiv}, year = {2022}, }
```
Getting Started
```python
from onto_merger.pipeline import Pipeline
initialise the pipeline
pipeline = Pipeline(projectfolderpath="../path/to/project")
run the process
pipeline.runalignmentandconnectionprocess()
view results in "../path/to/project/output/report/index.html"
```
Running tests
``` $ tox -e py
```
License
Credit
The Onto Merger logo is based on:
Owner
- Name: AstraZeneca
- Login: AstraZeneca
- Kind: organization
- Location: Global
- Website: https://www.astrazeneca.com/
- Repositories: 33
- Profile: https://github.com/AstraZeneca
Data and AI: Unlocking new science insights
GitHub Events
Total
- Watch event: 7
Last Year
- Watch event: 7
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| dgeleta | 8****a | 170 |
| Rozemberczki | k****8@a****t | 41 |
| Ughetto, Michaël | m****o@a****m | 2 |
| Michaël Ughetto | m****o@g****m | 1 |
Committer Domains (Top 20 + Academic)
Packages
- Total packages: 1
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Total downloads:
- pypi 6 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
pypi.org: onto-merger
OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes,(i.e. ontology concepts) that represent the *same domain*, e.g. diseases, and **connecting** them to form a single directed acyclic hierarchical graph (DAG) (i.e. an ontology class hierarchy).
- Homepage: https://github.com/AstraZeneca/onto_merger
- Documentation: https://onto-merger.readthedocs.io/
- License: Apache License, Version 2.0
-
Latest release: 0.1.0
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- Cython *
- Jinja2 ==3.0.0
- jupyter-sphinx *
- markupsafe ==2.0.1
- mock *
- nbconvert *
- nbsphinx *
- nbsphinx_link *
- numpy *
- pandas *
- six *
- sphinx *
- sphinx-autodoc-typehints *
- sphinx-automodapi *
- sphinx_rtd_theme *
- sphinxcontrib-pseudocode *
- tqdm *
- dataclasses-json ==0.5.7
- docopt ==0.6.2
- great_expectations ==0.15.2
- jinja2 ==3.0.3
- jsonschema ==4.4.0
- kaleido ==0.2.1
- networkit ==9.1.1
- networkx *
- numpy ==1.21.5
- pandas ==1.3.5
- pandas-profiling ==3.1.0
- plotly-express ==0.4.1
- pytest *
- pytest-cov *
- pytest-runner *
- ruamel.yaml ==0.17.17
- tqdm ==4.64.0
- dataclasses-json ==0.5.7
- docopt ==0.6.2
- great_expectations ==0.15.2
- jinja2 ==3.0.3
- jsonschema ==4.4.0
- kaleido ==0.2.1
- networkx *
- numpy ==1.21.5
- pandas ==1.3.5
- pandas-profiling ==3.1.0
- plotly-express ==0.4.1
- ruamel.yaml ==0.17.17
- tqdm ==4.64.0