Skip to main content

NetworkX for ontologies

Project description

NetworkX-based Python library for representing ontologies

GitHub Actions CI Build Status
Software License
Code style: black
PyPI

Summary

nxontology is a Python library for representing ontologies using a NetworkX graph. Currently, the main area of functionality is computing similarity measures between pairs of nodes.

Usage

Here, we'll use the example metals ontology:

Metals ontology from Couto & Silva (2011)

Note that NXOntology represents the ontology as a networkx.DiGraph, where edge direction goes from superterm to subterm. Currently, users must create their own networkx.DiGraph to use this package.

Given an NXOntology instance, here how to compute intrinsic similarity metrics.

from nxontology.examples import create_metal_nxo
metals = create_metal_nxo()
# Freezing the ontology prevents adding or removing nodes or edges.
# Frozen ontologies cache expensive computations.
metals.freeze()
# Get object for computing similarity, using the Sanchez et al metric for information content.
similarity = metals.similarity("gold", "silver", ic_metric="intrinsic_ic_sanchez")
# Access a single similarity metric
similarity.lin
# Access all similarity metrics
similarity.results()

The final line outputs a dictionary like:

{
    'node_0': 'gold',
    'node_1': 'silver',
    'node_0_subsumes_1': False,
    'node_1_subsumes_0': False,
    'n_common_ancestors': 3,
    'n_union_ancestors': 5,
    'batet': 0.6,
    'batet_log': 0.5693234419266069,
    'ic_metric': 'intrinsic_ic_sanchez',
    'mica': 'coinage',
    'resnik': 0.8754687373538999,
    'resnik_scaled': 0.48860840553061435,
    'lin': 0.5581154235118403, 
    'jiang': 0.41905978419640516,
    'jiang_seco': 0.6131471927654584,
}

Bibliography

Here's a list of alternative projects with code for computing semantic similarity measures on ontologies:

Below are a list of references related to ontology-derived measures of similarity. Feel free to add any reference that provides useful context and details for algorithms supported by this package. Metadata for a reference can be generated like manubot cite --yml doi:10.1016/j.jbi.2011.03.013. Adding CSL YAML output to media/bibliography.yaml will cache the metadata and allow manual edits in case of errors.

  1. Semantic Similarity in Biomedical Ontologies
    Catia Pesquita, Daniel Faria, André O. Falcão, Phillip Lord, Francisco M. Couto
    PLoS Computational Biology (2009-07-31) https://doi.org/cx8h87
    DOI: 10.1371/journal.pcbi.1000443 · PMID: 19649320 · PMCID: PMC2712090

  2. An Intrinsic Information Content Metric for Semantic Similarity in WordNet.
    Nuno Seco, Tony Veale, Jer Hayes
    In Proceedings of the 16th European Conference on Artificial Intelligence (ECAI-04), (2004) https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1065.1695

  3. Metrics for GO based protein semantic similarity: a systematic evaluation
    Catia Pesquita, Daniel Faria, Hugo Bastos, António EN Ferreira, André O Falcão, Francisco M Couto
    BMC Bioinformatics (2008-04-29) https://doi.org/cmcgw6
    DOI: 10.1186/1471-2105-9-s5-s4 · PMID: 18460186 · PMCID: PMC2367622

  4. Semantic similarity and machine learning with ontologies
    Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
    Briefings in Bioinformatics (2020-10-13) https://doi.org/ghfqkt
    DOI: 10.1093/bib/bbaa199 · PMID: 33049044

  5. Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language
    P. Resnik
    Journal of Artificial Intelligence Research (1999-07-01) https://doi.org/gftcpz
    DOI: 10.1613/jair.514

  6. An Information-Theoretic Definition of Similarity
    Dekang Lin
    ICML (1998) https://api.semanticscholar.org/CorpusID:5659557

  7. ontologyX: a suite of R packages for working with ontological data
    Daniel Greene, Sylvia Richardson, Ernest Turro
    Bioinformatics (2017-01-05) https://doi.org/f9k7sx
    DOI: 10.1093/bioinformatics/btw763 · PMID: 28062448 · PMCID: PMC5386138

  8. Metric of intrinsic information content for measuring semantic similarity in an ontology
    Md. Hanif Seddiqui, Masaki Aono
    Proceedings of the Seventh Asia-Pacific Conference on Conceptual Modelling - Volume 110 (2010-01-01) https://dl.acm.org/doi/10.5555/1862330.1862343
    ISBN: 9781920682927

  9. Disjunctive shared information between ontology concepts: application to Gene Ontology
    Francisco M Couto, Mário J Silva
    Journal of Biomedical Semantics (2011) https://doi.org/fnb73v
    DOI: 10.1186/2041-1480-2-5 · PMID: 21884591 · PMCID: PMC3200982

  10. A framework for unifying ontology-based semantic similarity measures: A study in the biomedical domain
    Sébastien Harispe, David Sánchez, Sylvie Ranwez, Stefan Janaqi, Jacky Montmain
    Journal of Biomedical Informatics (2014-04) https://doi.org/f52557
    DOI: 10.1016/j.jbi.2013.11.006 · PMID: 24269894

  11. Semantic Similarity in Cheminformatics
    João D. Ferreira, Francisco M. Couto
    IntechOpen (2020-07-15) https://doi.org/ghh2d4
    DOI: 10.5772/intechopen.89032

  12. An ontology-based measure to compute semantic similarity in biomedicine
    Montserrat Batet, David Sánchez, Aida Valls
    Journal of Biomedical Informatics (2011-02) https://doi.org/dfhkjv
    DOI: 10.1016/j.jbi.2010.09.002 · PMID: 20837160

  13. Semantic similarity in the biomedical domain: an evaluation across knowledge sources
    Vijay N Garla, Cynthia Brandt
    BMC Bioinformatics (2012-10-10) https://doi.org/gb8vpn
    DOI: 10.1186/1471-2105-13-261 · PMID: 23046094 · PMCID: PMC3533586

  14. Semantic similarity estimation in the biomedical domain: An ontology-based information-theoretic perspective
    David Sánchez, Montserrat Batet
    Journal of Biomedical Informatics (2011-10) https://doi.org/d2436q
    DOI: 10.1016/j.jbi.2011.03.013 · PMID: 21463704

  15. Ontology-based information content computation
    David Sánchez, Montserrat Batet, David Isern
    Knowledge-Based Systems (2011-03) https://doi.org/cwzw4r
    DOI: 10.1016/j.knosys.2010.10.001

  16. Leveraging synonymy and polysemy to improve semantic similarity assessments based on intrinsic information content
    Montserrat Batet, David Sánchez
    Artificial Intelligence Review (2019-06-03) https://doi.org/ghnfmt
    DOI: 10.1007/s10462-019-09725-4

  17. An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology
    Abhijit Adhikari, Biswanath Dutta, Animesh Dutta, Deepjyoti Mondal, Shivang Singh
    Journal of the Association for Information Science and Technology (2018-08) https://doi.org/gd2j5b
    DOI: 10.1002/asi.24021

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nxontology-0.1.2.tar.gz (31.5 kB view details)

Uploaded Source

Built Distribution

nxontology-0.1.2-py3-none-any.whl (22.0 kB view details)

Uploaded Python 3

File details

Details for the file nxontology-0.1.2.tar.gz.

File metadata

  • Download URL: nxontology-0.1.2.tar.gz
  • Upload date:
  • Size: 31.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.7

File hashes

Hashes for nxontology-0.1.2.tar.gz
Algorithm Hash digest
SHA256 3ce15dae42d8f562931b6aa2cd7f17e8c9ca09e6830550f3eaee7df538cd4f7d
MD5 b26a89743ded49d65d97ffdbc218fc34
BLAKE2b-256 8528fb7026970f1d5e560ca6885ba75eb1dc400eaa514ca7ca4e54d7b625e720

See more details on using hashes here.

File details

Details for the file nxontology-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: nxontology-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 22.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.7

File hashes

Hashes for nxontology-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 a827562393a361a1963844850b928a7c4a66478ad815a30506cf7b7d94388697
MD5 a0ee0402439d2d344874a037872c474d
BLAKE2b-256 18e984439cb7ce32b511e1997943b0760ff3f814f05710c3ab7c4ad70d355f0d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page