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5 results for “Ontology matching”
Matching Network of Ontologies: a Pattern Recognition Approach
<p>Networks of Ontologies research deals with the need to combine several ontologies at the same time. In a world of integrated systems (system of systems), isolated systems are increasingly rare in the near future, and their integration creates opportunities to change, validate information and add more value to an information system. This system of systems can contain ontologies to support the corresponding knowledge model. Consequently, new integration requirements may have to deal with network alignment rather than single ontologies. This work delves into the area of network alignment and proposes new ways to approach a particular case of alignment of large ontologies. The contribution of the work is the use of algebraic operations on networks to eliminate candidates before alignment and to use a stochastic search method to discover the relevant nodes. These nodes should be retained as they increase accuracy and final alignment retrieval even though they are identical and removed by the algebraic operation. To find out the particular relevance of each node, we propose a random walk combined with a frequent itemsets approach that overcomes the force brute approaches in processing time, as the size of networks grows, and have close precision. The approach was validated using networks of ontologies created from the OAEI ontologies. The approach selected the entities to send to the matcher without losing significant preexisting alignments. Finally, two different matchers were used to get metrics and compare the results with the pairwise force brute approach.</p>
Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
<p> </p> <blockquote> <p><strong>This version is used in the Bio-ML track of the OAEI 2024; the only change compared to the OAEI 2023 is the deletion of certain training subsumption mappings.</strong></p> </blockquote> <p> </p> <h3><strong>Overview</strong></h3> <p>The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching.</p> <p>There are five ontology pairs extracted from MONDO and UMLS:</p> <table> <tbody> <tr> <td>Source</td> <td>Task</td> <td>Category</td> <td>#SrcCls</td> <td>#TgtCls</td> <td>#Ref (equiv)</td> <td>#Ref (subs)</td> </tr> <tr> <td>Mondo</td> <td>OMIM-ORDO</td> <td>Disease</td> <td>9,648</td> <td>9,275</td> <td>3,721</td> <td>103</td> </tr> <tr> <td>Mondo</td> <td>NCIT-DOID</td> <td>Disease</td> <td>15,762</td> <td>8,465</td> <td>4,686</td> <td>3,338 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-FMA</td> <td>Body</td> <td>34,418</td> <td>88,955</td> <td>7,256</td> <td>5,453 (-53)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Pharm</td> <td>29,500</td> <td>22,136</td> <td>5,803</td> <td>4,224 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Neoplas</td> <td>22,971</td> <td>20,247</td> <td>3,804</td> <td>213</td> </tr> </tbody> </table> <p>The "-" numbers reflect the changes due to lthe deletion of certain training subsumption mappings.</p> <p>The main track is available at "bio-ml", where each pair is associated with a task folder, containing the source and target ontologies, reference equivalence mappings (in "refs_equiv"), reference subsumption mappings ("refs_subs"). </p> <p>The special sub-track is available at "bio-llm", where each pair is associated with a task folder, containing the source and target ontologies, and the test candidate mappings. </p> <p> </p> <h3><strong>Citation</strong></h3> <p><strong>Bio-ML (Main Track)</strong></p> <pre>```<br>@inproceedings{he2022machine, title={Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian}, booktitle={International Semantic Web Conference}, pages={575--591}, year={2022}, organization={Springer} }<br>```</pre> <p><strong>Bio-LLM (Sub-track)</strong></p> <pre>```<br>@article{he2023exploring, title={Exploring large language models for ontology alignment}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian}, journal={arXiv preprint arXiv:2309.07172}, year={2023} }<br>```</pre> <p> </p> <h3><strong>Important Links</strong></h3> <ul> <li>See detailed documentation at: <a href="https://krr-oxford.github.io/DeepOnto/bio-ml">https://krr-oxford.github.io/DeepOnto/bio-ml</a>.</li> <li>See the OAEI Bio-ML track at: <a href="https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/">https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/</a></li> <li>See our resource paper for the original Bio-ML at <a href="https://arxiv.org/abs/2205.03447">arxiv</a> or <a href="https://link.springer.com/chapter/10.1007/978-3-031-19433-7_33">springer</a> (accepted at <em>ISWC-2022</em> and nominated as the <em>best resource paper candidate</em>). See our poster paper for the Bio-LLM sub-track at <a href="https://arxiv.org/abs/2309.07172">arxiv </a>(accepted at <em>ISWC-2023 Posters & Demos</em>).</li> </ul> <p> </p> <h3><strong>Changelog</strong></h3> <p>The only change in this version compared to the OAEI 2023 is the deletion of certain training subsumption mappings that can be directly exploited through deductive reasoning.</p>
Pharmacogenomics datasets for Ontology Matching
<p><strong>Pharmacogenomics datasets for Ontology Matching</strong></p> <p>Pharmacogenomics (or PGx for short) involves n-ary tuples representing so-called "pharmacogenomic relationships" and their components of three distinct types: drugs, genetic factors, and phenotypes. Tuples are reified as instances of the class ``pgxo:PharmacogenomicRelationship``. The goal of the matching task is to match these tuples (instance matching).</p> <p>Motivation: Pharmacogenomic tuples involve drugs, genetic factors, and phenotypes, and state that patients being treated by the specified drugs while having the specified genetic factors may experience the given phenotypes. Knowledge in pharmacogenomics is scattered across several resources, e.g., reference databases (<a href="https://www.pharmgkb.org/">PharmGKB</a>) or the biomedical literature. Hence, there is a need to build a consolidated view of the knowledge of this domain by aligning tuples from different sources. See [1] for a detailed motivation and [2] for a detailed task description.</p> <p><strong>Datasets</strong></p> <p>We provide different subsets of the alignments available in <a href="https://pgxlod.loria.fr">PGxLOD</a> that have been created with the matching rules described in [3].</p> <p><em>Task with 10 % of PGx relationships</em></p> <ul> <li>Alignments: 942 <ul> <li>relatedMatch alignments: 83</li> <li>sameAs alignments: 315</li> <li>closeMatch alignments: 46</li> <li>broadMatch alignments: 332</li> <li>narrowMatch alignments: 166</li> </ul> </li> <li>Entities to align in source: 2523</li> <li>Triples in source: 211770</li> <li>Entities to align in target: 2520</li> <li>Triples in target: 208051</li> </ul> <p><em>Task with 50 % of PGx relationships</em></p> <ul> <li>Alignments: 23938 <ul> <li>relatedMatch alignments: 1500</li> <li>sameAs alignments: 8636</li> <li>closeMatch alignments: 1128</li> <li>broadMatch alignments: 8047</li> <li>narrowMatch alignments: 4627</li> </ul> </li> <li>Entities to align in source: 12767</li> <li>Triples in source: 459483</li> <li>Entities to align in target: 12450</li> <li>Triples in target: 450364</li> </ul> <p><em>Task with 100 % of PGx relationships</em></p> <ul> <li>Alignments: 88569 <ul> <li>relatedMatch alignments: 5318</li> <li>sameAs alignments: 34510</li> <li>closeMatch alignments: 3978</li> <li>broadMatch alignments: 24097</li> <li>narrowMatch alignments: 20666</li> </ul> </li> <li>Entities to align in source: 25362</li> <li>Triples in source: 691151</li> <li>Entities to align in target: 25073</li> <li>Triples in target: 687052</li> </ul> <p><strong>References</strong></p> <ol> <li>Pierre Monnin, Joël Legrand, Graziella Husson, Patrice Ringot, Andon Tchechmedjiev, Clément Jonquet, Amedeo Napoli, Adrien Coulet: PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison. BMC Bioinformatics 20-S(4): 139:1-139:16 (2019) <a href="https://doi.org/10.1186/s12859-019-2693-9">[pdf]</a></li> <li>Pierre Monnin, Adrien Coulet: Matching pharmacogenomic knowledge: particularities, results, and perspectives. OM@ISWC 2022: 79-83 <a href="https://ceur-ws.org/Vol-3324/om2022_STpaper3.pdf">[pdf]</a></li> <li>Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Adrien Coulet: Knowledge-Based Matching of n-ary Tuples. ICCS 2020: 48-56 <a href="https://inria.hal.science/hal-02493067/document">[pdf]</a></li> </ol>
Generated ontology matching tests and matcher results
This archive contains all the files supporting the results published in [1] using the Alignment API [2]. This consists of generating tests from ontology matchers using a seed ontologies, running ontology matchers on these tests and evaluating their results. In order to facilitate reproducibility, scripts to obtain and analyse the results are provided.<br>The archive contains:<ul><li>scripts to generates the tests</li><li>scripts to evaluate the results</li><li>a directory of the original benchmarks data set</li><li>a directory of the four seed ontologies</li><li>a directory of the generated datasets (ontologies and reference alignments) and the alignments obtained by 10 matchers</li><li>a directory of the analysed results.</li></ul><br>More information is available in the paper [1] itself.<br>[1] Jérôme Euzenat, Maria Roşoiu, Cássia Trojahn dos Santos, Ontology matching benchmarks: generation, stability, and discriminability, <i>Journal of web semantics</i> 21:30-48, 2013 [DOI:<a href="https://doi.org/10.1016/j.websem.2013.05.002">10.1016/j.websem.2013.05.002 </a>]<br>[2] <a href="https://gitlab.inria.fr/moex/alignapi/">https://gitlab.inria.fr/moex/alignapi/</a><br><br>
Data from: Matching arthropod anatomy ontologies to the Hymenoptera Anatomy Ontology: results from a manual alignment
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