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287 results for “ontology”
The BrainTeaser Ontology for ALS and MS Clinical Data
<p>This repository contains the <strong>BrainTeaser Ontology</strong> (BTO) whose purpose is to jointly model both <em>Amyotrophic Lateral Sclerosis (ALS)</em> and <em>Multiple Sclerosis (MS)</em>.</p> <p>BTO serves multiple purposes:</p> <ul> <li>to provide a unified and comprehensive conceptual view about ALS and MS, which are typically dealt with separately, allowing us to coherently integrate the data coming from the different medical partners in the project;</li> <li>to seamlessly represent both <em>retrospective</em> data and <em>prospective</em> data, produced during the lifetime of BRAINTEASER</li> <li>to allow for sharing and re-using the BRAINTEASER datasets according to Open Science and FAIR principles.</li> </ul> <p>BTO is innovative since it relies on very few seed concepts - Patient, Clinical Trial, Disease, Event - which allow us to jointly model ALS and MS and to grasp the time dimension entailed by the progression of such diseases.</p> <p>Indeed, the core idea is that a Patient participates in a Clinical Trial, suffers from some Diseases, and undergoes Events. These Events are different in nature and cover a wide range of cases, e.g. Onset, Pregnancy, Symptom, Trauma, Diagnostic Procedure (like evoked potentials or ALS-FRS questionnaires) Therapeutic Procedure (like Mechanical Ventilation for ALS or Disease-Modifying Therapy for MS), Relapse, and more. Overall, this event-based approach allows us to model ALS and MS in an unified way, sharing concepts among these two diseases, and to track what happens during their progression. </p> <p>The full BTO documentation can be accessed at <a href="https://brainteaser.dei.unipd.it/ontology/">https://brainteaser.dei.unipd.it/ontology/</a>.</p> <p>The BTO has been used to share the <a title="BRAINTEASER ALS and MS Datasets" href="https://doi.org/10.5281/zenodo.12789962" target="_blank" rel="noopener">BRAINTEASER ALS and MS Datasets</a>.</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>
Ontology based mining of pathogen-disease associations from literature
<p>This dataset contains a number of manually evaluated text mined pathogen-disease associations for the study described at</p> <p>doi: https://doi.org/10.1101/437558</p>
The RSE Tools Ontology
<p>This ontology contains classes to descibe Little Minions as Tools, Linked Pipes a Workflow for Linked Data Tools and Data Dragons as Repository for Linked Data.</p>
Dividing the Ontology Alignment Task
<p>Large ontologies still pose serious challenges to state of the art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively segment an input ontology matching task into smaller and more tractable (sub)matching tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are adequate in practice and can be integrated within the workflow of state of the art systems.</p>
The LUCKINet land use ontology
<h1>1. Rationale</h1> <p>The LUCKINet land use ontology expands <a href="https://agrovoc.fao.org/browse/agrovoc/en">AGROVOC</a>, developed by the Food and Agriculture Organization of the United Nations (FAO). Like AGROVOC, the LUCKINet land use ontology uses semantic web technology powered by the Simple Knowledge Organization System (<a href="https://www.w3.org/TR/skos-primer/">SKOS</a>).</p> <p>The ontology has been primarily developed to support the LUCKINet effort to standardize approaches that develop gridded information on land use dynamics. As standards develop from the needs of various stakeholders, we have designed this ontology in a way that it can grow organically with emerging use cases. The focal unit is the crop or livestock species, which makes it easy to align our ontology with other vocabularies and typologies. The ontology is distinguished into two main sections: landcover and land-use concepts.</p> <p>We built the ontology with a strong focus on mapping land-use concepts to FAO/AGROVOC concepts to foster interoperability with the wealth of other projects building on AGROVOC. Moreover, concepts from different state-of-the-art datasets, such as land use from statistical agencies worldwide and the (grey) literature, are considered.</p> <p>You will find landcover concepts in this ontology in an unfamiliar way. In many projects, landcover is an amalgamation of concepts across various functional types of concepts. For example, 'cropland' is often regarded as a landcover, but because it also includes human activities managing that cover, it is not a pure <em>cover</em> but also a <em>use</em> type. We, therefore, revised the former version of this ontology, which was heavily centred on the <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE Land Cover</a> classification, to be more consistent from a semantic point of view. Any level now distinguishes only one conceptual dimension. The ontology is now distinguished into land cover, dynamic and use. <em>Cover</em> is characterized purely by the material or entities covering the surface, <em>dynamics</em> are characterized by the most dominant process (such as management or productiveness), and <em>use</em> is characterized by how the sphere covering the surface is managed and what is used.</p> <h1>2. Method</h1> <p>The ontology was built with the R-package <a href="https://cran.r-project.org/web/packages/ontologics/index.html">ontologics v0.7.3</a>, which enables reproducibility and transparency in creating an ontology. A script documenting this process is part of this publication.</p> <p>Concepts were taken from the external datasets harmonized during the LUCKINet and Global Pasture Watch project (the harmonized data are published on zenodo).</p> <h2>2.1 Sources</h2> <p>The ontology is an amalgamation of various datasets:</p> <ol> <li>The <a href="https://datalab.review.fao.org/datalab/caliper/web/classification-page/43">FAO Indicative Crop Classification</a> (ICC) v1.1 provides a hierarchical structure of crop and livestock concepts.</li> <li>The <a href="https://www.fao.org/faostat/en/#data/QCL">FAO Crops and livestock products</a> (QCL) dataset contains a range of concepts used by the FAO and frequently by statistical offices of nations that make their census data publicly available.</li> <li>The <a href="https://www.fao.org/faostat/en/#data/RL">FAO Land Use</a> (RL) dataset contains a hierarchical set of land-use concepts that represent the most harmonized representation of land-use concepts to date.</li> <li>The <a href="https://www.fao.org/3/I8661EN/i8661en.pdf">FAO Forest Resource Assessment</a> (FRA) contains a detailed set of forest classes that fit well with the FAO Land Use vocabulary and represent relevant classes at a coarse scale (Sections 1b, 1e, 1f).</li> <li><a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/">CORINE Land Cover</a> (CLC) classes are used as the basis to define the (hierarchical) organization of land cover and land use classes (into which the crop and livestock commodities are nested).</li> </ol> <h2>2.2 Classes</h2> <p>The ontology is hierarchically structured into the following classes:</p> <ul> <li>domain <ul> <li>landcover <ul> <li>land dynamic <ul> <li>land use</li> </ul> </li> </ul> </li> <li>production <ul> <li>group <ul> <li>class <ul> <li>crop</li> <li>animal</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li> domain: the overarching dimensions into which all other concepts are nested. These represent conceptually different kinds of classification systems (landcover and production).</li> <li>landcover: surface area described by the predominant landcover in that location.</li> <li>production: surface area described by the crops and livestock produced in that location.</li> <li>land dynamic: surface area described by the predominant attribute determining dynamics within the landcover type.</li> <li>land use: surface area described by mutually exclusive types of how land covered by a particular type and dynamic is used.</li> <li>group: broad groups of concepts that describe crops and livestock.</li> <li>class: mutually exclusive types of concepts that describe crops and livestock.</li> <li>crop/animal: individual commodities (typically at species level).</li> </ul> <h2>2.3 Concepts</h2> <p>The bulk of the ontology consists of a collection of concepts, each an instance of a specific class. The concepts also bear hierarchical relations determined by their class affiliation. For example, the concept of <em>Cultivated grazingland</em> (class: use) is semantically narrower than <em>Managed vegetation</em> (class: dynamic), which in turn is narrower than the concept of <em>VEGETATED LAND</em> (class: lancover), which is narrower than <em>landcover</em> (class: domain).</p> <h2>2.4 Mappings</h2> <p>As far as possible, concepts are mapped to all corresponding wikidata and GBIF concepts. For example, the concept of <em>maize</em> is mapped to wikidata concepts <a href="https://www.wikidata.org/wiki/Q11575">Q11575</a> and <a href="https://www.wikidata.org/wiki/Q25618328">Q25618328</a> and GBIF concept <a href="https://www.gbif.org/species/5290052">5290052</a>. Moreover, concepts are mapped to the</p> <ul> <li>life-form concepts <em>graminoid</em>, <em>forb</em>, <em>herb</em>, <em>shrub</em>, <em>tree</em></li> <li>purpose concepts <em>energy</em>, <em>fibre</em>, <em>food</em>, <em>wood</em>, <em>forage</em>, <em>silage</em>, <em>fodder</em>, <em>industrial</em>, <em>recreation</em>, <em>medicinal </em>and <em>labour </em>and <em>reproduction</em></li> <li>used-part concepts <em>bark</em>, <em>bast</em>, <em>biomass</em>, <em>buds</em>, <em>bulb</em>, <em>eggs</em>, <em>flower</em>, <em>fruit</em>, <em>hair</em>, <em>honey</em>, <em>husk</em>, <em>leaves</em>, <em>lint</em>, <em>meat</em>, <em>dairy</em>, <em>resin</em>, <em>root</em>, <em>sap</em>, <em>seed</em>, <em>shoot</em>, <em>stalk</em>, <em>time</em>, <em>tuber</em>, <em>fur </em>and <em>silk</em></li> <li>agronomic attributes such as the <em>years-before-harvest</em> and <em>years-of-harvest</em> (i.e., for how many years a crop, once mature, can be harvested)</li> </ul> <h1>3. Files</h1> <p>- <code>make_ontology.R</code>: The script used to build this ontology<br>- <code>luckiOnto.rds</code>: the ontology to access and manipulate with R and the ontologics R-package</p> <h1>4. Version changes</h1> <ul> <li>uses a new version of <code>ontologics</code></li> <li>revised hierarchy of landcover concepts</li> <li>includes livestock concepts</li> <li>no mapping to 'persistence' concepts anymore; instead, years-before-harvest (YBH) and years-of-harvest (YOH) are introduced.</li> <li>various rare species are now included</li> </ul> <h1>5. Acknowledgements</h1> <p>This work was supported by funding to Carsten Meyer through the Flexpool mechanism of the German Centre for Integrative Biodiversity Research (iDiv) (FZT-118, DFG). Several developments in this software stem from work on the GeoKur research data infrastructure. GeoKur is granted by the BMBF (Federal Ministry of Education and Research) under number 16QK04A. Revisions of the ontology pertaining to the landcover hierarchy and livestock concepts were made during the initial funding period of the Global Pasture Watch Project.</p>
EB_HQ: Knowledge Graph of the First Eight Editions of the Encyclopaedia Britannica (1768-1860) Following the Heritage Textual Ontology
<p>This EB_HQ Knowledge Graph, represents information from the first eight editions of the <em>Encyclopaedia Britannica</em> (1768-1860), structured according to the <a href="https://eur02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fw3id.org%2Fhto&data=05%7C02%7C%7C9596c1d96d154755723e08dce92a686d%7C2e9f06b016694589878910a06934dc61%7C0%7C0%7C638641615571225885%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=vr0NRZPBmtKGFCs2gliaBKJ6K%2FCCrwEEv%2FlrWfNLRpU%3D&reserved=0">Heritage Textual Ontology </a>(HTO). This version enhances the information from the previously developed <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecords%2F6673897&data=05%7C02%7C%7C9596c1d96d154755723e08dce92a686d%7C2e9f06b016694589878910a06934dc61%7C0%7C0%7C638641615571238085%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=HHP1VKXayraELBzHQEhpya01JM1vGV6YY7Zrgz8rhkg%3D&reserved=0">EB-KG</a> by unifying data across various sources (such as <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdata.nls.uk%2Fdata%2Fdigitised-collections%2Fencyclopaedia-britannica%2F&data=05%7C02%7C%7C9596c1d96d154755723e08dce92a686d%7C2e9f06b016694589878910a06934dc61%7C0%7C0%7C638641615571251754%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=4pRrz54ZNf4gPOH20g4apwPxbl3LHknXpbUyGssV7R0%3D&reserved=0">National Library of Scotland</a>, or <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftu-plogan.github.io%2Fsource%2Fr_7th_edition.html&data=05%7C02%7C%7C9596c1d96d154755723e08dce92a686d%7C2e9f06b016694589878910a06934dc61%7C0%7C0%7C638641615571265383%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aoDV%2BSnXJ%2B3yzI7rsP%2BCdhAXERsu8WVUSA2xtbw6vHE%3D&reserved=0"> Nineteenth-Century Knowledge Project</a> ), editions, allowing for more comprehensive tracking of the evolution of concepts over time. For each edition, the highest-quality text source is selected to ensure optimal content. It integrates advanced information extraction methods, deep-learning-based knowledge enrichment, facilitating richer analyses. </p> <p>The EB_HQ captures 3316459 RDF triples, providing structured metadata and descriptions for each edition, volume, and term. It unifies information across various editions, allowing seamless tracking of the evolution of concepts over time. Data enrichment includes semantic linkages to external knowledge bases like DBpedia and Wikidata, facilitating broader analysis and connectivity to contemporary information.</p> <p>This dataset supports historical research, offering rich semantic data for researchers exploring the evolution of knowledge and concepts in the <em>Encyclopaedia Britannica</em>. It features terms categorized as <em>Articles</em> or <em>Topics</em>, each with detailed metadata extracted from METS and ALTO XML files. </p>
EB_Composite: Knowledge Graph of the First Eight Editions of the Encyclopaedia Britannica (1768-1860) Following the Heritage Textual Ontology
<p>The EB_Composite Knowledge Graph represents information from the first eight editions of the <em>Encyclopaedia Britannica</em> (1768–1860), structured using the <a href="https://w3id.org/hto"><em>Heritage Textual Ontology</em></a> (HTO). It extends our previously developed <a href="https://zenodo.org/records/13919115">EB_HQ</a> by incorporating multiple text sources, such as the <a href="https://data.nls.uk/data/digitised-collections/encyclopaedia-britannica/">National Library of Scotland</a> and the <a href="https://tu-plogan.github.io/source/r_7th_edition.html">Nineteenth-Century Knowledge Project</a>, for each edition. A particular source is added, comprising of post-corrected textual content generated using deep-learning-based OCR error correction methods. These sources presents different levels of text quality, and EB_Composite enables researchers to compare these texts, and track how they are digitised or extracted from these sources.</p> <p>The EB_Composite captures 4150776 RDF triples. Same as EB_HQ, it provides structured metadata and descriptions for each edition, volume, and term. By integrating information across different editions, it enables smooth tracking of concept evolution over time. Additionally, the dataset features semantic connections to external knowledge bases such as DBpedia and Wikidata, enhancing links to modern information and supporting more comprehensive analyses.</p> <p>Designed to support historical research, this dataset offers rich semantic data for exploring the development of knowledge and concepts in the <em>Encyclopaedia Britannica</em>. It categorizes terms as either <em>Articles</em> or <em>Topics</em>, each with detailed metadata extracted from METS and ALTO XML files. OCR errors common in historical texts have been mitigated using deep-learning-based corrections.</p>
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Refining Bulk Segregant Analyses: Ontology-Mediated Discovery of Flowering Time Genes in Brassica oleracea
<p>This record comprises the main supplement to study <a href="https://doi.org/10.1101/2021.08.11.455982">10.1101/2021.08.11.455982</a>. Contained in the archive identified by this record are the following files:</p> <ul> <li>SUPPLEMENTARY_METHODS.pdf contains an extended description of the methods of the study, including external links to raw, intermediate, and result data</li> <li>st[1-5]_* supplementary tables referenced in the main manuscript body of the study</li> <li>*.zip files contain intermediate data archives referenced in the supplementary methods, *.txt files with the same names list the contents of the zip files </li> <li>go-basic.obo is an unmodified copy of the Gene Ontology (in OBO format) as used in this study</li> </ul> <p><strong>NOTE 1</strong>: this record contains additional references (related identifiers) that identify all other data and source code used in this study</p> <p><strong>NOTE 2</strong>: for this record and all related records, the authors, their affiliations and their respective funding as relevant to this project are identified in the main manuscript, whose contents take precedence over equivalent metadata fields in supplementary records.</p>
Ontology files in .owl, rdf and xml format demonstrating the texts, documents and works (=entities) ontology
<p>See various writings by Robinson concerning this ontology (e.g. , <a href="https://wiki.usask.ca/pages/viewpage.action?pageId=1324745355">Creating and Implementing an Ontology of Documents and Texts (ADHO 2018)</a>.</p> <p>Note revision of 10/21: removal of parts of document, work, text. dc:hasPart and dc:isPartOf make this redundant.</p>
Diabetes Pharmacology Ontology
<p>The Diabetes Pharmacology Ontology contains the classes needed to describe antihyperglycemic therapies, pharmacotherapy factors, and diagnostic factors.</p>
Tonalities ontology
<p>The ontology module TONALITIES, which is part of the Polifonia Ontology Network, represents concepts and relationships relating to music analysis and music-theoretical knowledge. The ontology aims at capturing the inner organization of musical works described on the basis of cadences, ambitus, imitation points, alterations, chromaticism, roots, harmonic progressions, etc. The strategy adopted is not to provide a single authoritative model, but rather to let coexist different models corresponding to different music-theoretical perspectives. This multifaceted approach helps to understand how different theoretical viewpoints, for instance on modality or tonality, affect the analytical understanding of the same musical work. The different theoretical models are imported into a main ontology that provides classes related to composers, musical works and their encoding, theoretical models and their analytical application.</p>
OntoLAMA: LAnguage Model Analysis for Ontology Subsumption Inference
<h3><strong>About</strong></h3> <p>OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference. The work follows the "LMs-as-KBs" literature but focuses on conceptualised knowledge extracted from formalised KBs such as the OWL ontologies. Specifically, the subsumption inference (SI) task is introduced and formulated in the Natural Language Inference (NLI) style, where the sub-concept and the super-concept involved in a subsumption axiom are verbalised and fitted into a template to form the premise and hypothesis, respectively. The sampled axioms are verified through ontology reasoning. The SI task is further divided into Atomic SI and Complex SI where the former involves only atomic named concepts and the latter involves both atomic and complex concepts. Real-world ontologies of different scales and domains are used for constructing OntoLAMA and in total there are four Atomic SI datasets and two Complex SI datasets.</p> <p> </p> <table> <tbody> <tr> <th>Dataset Source</th> <th>#Concepts</th> <th>#EquivAxioms</th> <th>#Datasets(Train/Dev/Test)</th> </tr> </tbody> <tbody> <tr> <td>Schema.org</td> <td>894</td> <td>N/A</td> <td> <p>Atomic SI: 808/404/2, 830</p> </td> </tr> <tr> <td>DOID</td> <td>11,157</td> <td>N/A</td> <td> <p>Atomic SI: 90,500/11,312/11,314</p> </td> </tr> <tr> <td>FoodOn</td> <td>30,995</td> <td>2,383</td> <td> <p>Atomic SI: 768,486/96,060/96,062</p> <p>Complex SI: 3,754/1,850/13,080</p> </td> </tr> <tr> <td>GO</td> <td>43,303</td> <td>11,456</td> <td> <p>Atomic SI: 772,870/96,608/96,610</p> <p>Complex SI: 72,318/9,040/9,040</p> </td> </tr> <tr> <td>MNLI</td> <td>N/A</td> <td>N/A</td> <td> <p>biMNLI: 235,622/26,180/12,906</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Citation</strong></h3> <p>The relevant paper has been accepted at Findings of ACL 2023: <a href="https://aclanthology.org/2023.findings-acl.213/">https://aclanthology.org/2023.findings-acl.213/</a>.</p> <pre>```<br>@inproceedings{he2023language, title={Language Model Analysis for Ontology Subsumption Inference}, author={He, Yuan and Chen, Jiaoyan and Jimenez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian}, booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, pages={3439--3453}, year={2023} }<br>```</pre> <h3><strong>Links</strong></h3> <ul> <li>See instructions at: <a href="https://krr-oxford.github.io/DeepOnto/ontolama/">https://krr-oxford.github.io/DeepOnto/ontolama/</a></li> <li>We have made available a convenient access of these datasets through Huggingface: <a href="https://huggingface.co/datasets/krr-oxford/OntoLAMA">https://huggingface.co/datasets/krr-oxford/OntoLAMA</a></li> <li>The arxiv version is available at: <a href="https://arxiv.org/abs/2302.06761">https://arxiv.org/abs/2302.06761</a></li> </ul> <h3><strong>Contact</strong></h3> <p>Yuan He (<code>yuan.he(at)cs.ox.ac.uk</code>)</p>
Ontology for the Anatomy of the Insect SkeletoMuscular system
<p>The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multi-species anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to (1) use controlled vocabularies and create semi-automated computer-parsable insect morphological descriptions; (2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and (3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.</p>
CRMSurv Ontology RDF
<p>This ontology extends the CIDOC CRM 6.2 ontology data standard for cultural heritage data in order to provide classes and properties necessary to describe unique aspects of the archaeological survey process. It also makes use of classes and properties of the CIDOC CRM extensions CRMArchaeo 1.4.1 and CRMsci 1.2.3. The ontology is intended to support researchers interested in integrating archaeology survey data using the CIDOC CRM.</p>
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
<p>This is the repository for ISWC 2023 Resource Track submission for <code>Text2KGBench: Benchmark for Ontology-Driven Knowledge Graph Generation from Text</code>. Text2KGBench is a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences.</p> <p>It contains two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences.</p> <p><strong>An example</strong></p> <p>An example test sentence:</p> <pre><code>Test Sentence: {"id": "ont_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King."} </code></pre> <p>An example of ontology:</p> <p>Ontology: <a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies/owl/ont_2_music.ttl">Music Ontology</a></p> <p>Expected Output:</p> <pre><code>{ "id": "ont_k_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King.", "triples": [ { "sub": "The Loco-Motion", "rel": "publication date", "obj": "01 January 1962" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Gerry Goffin" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Carole King" },] } </code></pre> <p>The data is released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY 4.0) License.</p> <p>The structure of the repo is as the following.</p> <ul> <li>Text2KGBench <ul> <li>src: the source code used for generation and evaluation, and baseline <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/benchmark"><code>benchmark</code></a> the code used to generate the benchmark</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation"><code>evaluation</code></a> evaluation scripts for calculating the results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation">baseline</a> code for generating the baselines including prompts, sentence similarities, and LLM client.</li> </ul> </li> <li>data: the benchmark datasets and baseline data. There are two datasets: wikidata_tekgen and dbpedia_webnlg. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen">wikidata_tekgen</a> Wikidata-TekGen Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies">ontologies</a> 10 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/manually_verified_sentences">manually_verified_sentences</a> ids of a subset of test cases manually validated</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences">unseen_sentences</a> new sentences that are added by the authors which are not part of Wikipedia <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/test">test unseen</a> test unseen test sentences</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/ground_truth">ground_truth</a> ground truth for unseen test sentences.</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts">prompts</a> prompts corresponding to each test file <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts/unseen">unseen prompts</a> unseen prompts for the unseen test cases</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen">unseen results</a> results for the unseen test cases <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg">dbpedia_webnlg</a> DBpedia Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ontologies">ontologies</a> 19 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/prompts">prompts</a> prompts corresponding to each test file</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>This benchmark contains data derived from the TekGen corpus (part of the KELM corpus) [1] released under CC BY-SA 2.0 license and WebNLG 3.0 corpus [2] released under CC BY-NC-SA 4.0 license.</p> <p>[1] Oshin Agarwal, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2021. Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3554–3565, Online. Association for Computational Linguistics.</p> <p>[2] Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017. Creating Training Corpora for NLG Micro-Planners. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 179–188, Vancouver, Canada. Association for Computational Linguistics.</p>
Taxonomic representation of the Ontology
<p>Base material for the construction of OntoMaturity.</p>
AGILE 4.0 MBSE Ontology
<p>This resource contains the Model Based System Engineering (MBSE) ontology being developed within the context of the H2020 EU Project AGILE 4.0 (2019-2022), and published as open access.</p> <p>The available files represent the meta-models, rendered by OWL, supporting the development of complex systems.</p> <p>The domains addressed by the meta-models are expanded, revised and maintained during the project.</p> <p> </p> <p>The ontology uploaded onto this page has been developed to address the development of complex aeronautical systems investigated in AGILE 4.0 Project, but it can be used to address any other kind of system.</p> <p> </p> <p>Domains currently included in the ontology:</p> <ul> <li>April 2021: meta-model for the modeling of system requirements. Additional information can be found in the project deliverable D1.3 "Process definition A4F for Application Case definition" (open access).</li> <li>June 2023: meta-model for the modeling of the whole system development process, including modeling of requirements, architectures and system design and optimization. Additional information can be found in the project deliverable D3.3 "AGILE 4.0 operational and trade-off process" (open access).</li> </ul>
Ontology metadata categorization
<p>This file summarizes an analysis aimed at finding the support for each MOD metadata category by existing ontologies and vocabularies. For each property, we map it against the most relevant MOD metadata category to find their support.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.