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287 results for “Ontology”
EOL v3 data model Ontologies: occurrence_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: measurement_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: media_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: reference_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: agent_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
UnientrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation Benchmarks with Unified Entrez Gene Identifiers
<p>Our work focuses on providing a comprehensive dataset and benchmarks for evaluating gene ontology annotations using a unified system of Entrez Gene Identifiers.</p>
Verifying Ontology Restrictions: Pizza Axioms
<p>Data created fot the experimental investigations performed in [1] and [2]. </p> <ul> <li><strong><em>PizzaExperiment-Dataset-HIT-Sources.xlsx</em></strong> contains 90 Examples (30 Ontology Axioms, represented in 3 formalism) [1] <ul> <li>For each axioms a correct and a corrupted version is provided with indication of the concrete defect type</li> </ul> </li> <li><em><strong>PizzaExperiment-Dataset-GPT Annottaions.xlsx </strong>-</em> an extension of the previuos dataset, inlcuding an additional TTL-representational formalism as well as GPT-4 annotations of each axiom [2].</li> </ul> <p>[1] <span>S. Tsaneva</span> and <span>M. Sabou</span>, “Enhancing Human-in-the-Loop Ontology Curation Results through Task Design,” Journal of Data and Information Quality, 2023.</p> <p>[2] <span>S. Tsaneva</span>, S. Vasic, and <span>M. Sabou</span>, “LLM-driven Ontology Evaluation: Verifying Ontology Restrictions with ChatGPT,” in The Semantic Web: ESWC Satellite Events, 2024.</p> <p> </p>
Inference Ontology
<p>Overview</p> <p>The diagrams showcase an inference ontology designed to model the provenance of any proposition or statement. Although originally developed within the context of the <a href="https://github.com/GOLEM-lab/golem-ontology">GOLEM framework</a>, which has since adopted a different approach, this module remains applicable across various digital humanities domains by focusing on the inference-making process for information, utilizing the <a href="https://www.cidoc-crm.org/crminf/fm_releases">CRMinf ontology.</a></p> <p>Scope<br>The Inference Module addresses three key aspects of inference:</p> <ul> <li>Source for Inference: The origin or basis of the inference.</li> <li>Method of Inference: The logic or methodology applied to derive the inference.</li> <li>Premise for Inference: Other statements or propositions that the inference is based on.</li> </ul> <p>Ontology (Diagram)<br>In this ontology, any statement is represented as crminf:I2_Belief. The formal representation of this belief is encapsulated in crminf:I4_Proposition_Set, which can be:</p> <ul> <li>A single RDF triple (atomic proposition).</li> <li>Multiple triples, or an entire knowledge graph (composite proposition).</li> </ul> <p>The core of these statements is structured based on the subject–predicate–object framework. RDF reification is employed to treat the entire statement as the subject in the inference triple.</p> <p>Key Components</p> <ul> <li>Belief: Concluded from the inference process, represented by crminf:I5_Inference_Making.</li> <li>Source of Inference: Defined by crm:E73_Information_Object, which may be a text, metadata, or any other form of information.</li> <li>Method of Inference: Captured by crminf:I3_Inference_Logic, which can be associated with crm:E55_Type to specify methods like manual annotation or machine learning.</li> <li>Premise: Other beliefs or statements upon which the inference relies, formalized by crminf:I4_Proposition_Set.</li> </ul> <p>Classes</p> <ul> <li>crminf:I2_Belief: Represents any proposition within the ontology.</li> <li>crminf:I4_Proposition_Set: The formal representation of a belief.</li> <li>crminf:I5_Inference_Making: The process of making an inference for any proposition.</li> <li>crminf:I3_Inference_Logic: The methodology or logic used in the inference.</li> <li>crm:E73_Information_Object: The source of inference, specified by subclasses such as lrm:F2_Expression for text or</li> <li>crm:E33_Linguistic_Object for metadata.</li> <li>rdf:Statement: Each crminf:I4_Proposition_Set is also an rdf:Statement, allowing specification of the subject, predicate, and object using rdf:subject, rdf:predicate, and rdf:object.</li> </ul> <p>Properties</p> <ul> <li>crminf:J4_that: Links a belief to its formal representation.</li> <li>crminf:J2_concluded_that: Links a belief to the inference that concluded this belief.</li> <li>crminf:J3_applies: Links an inference process to its method.</li> <li>crminf:J1_used_as_premise: Links the belief used as a premise for another belief.</li> <li>crm:P16_used_specific_object: Links the source to the inference process.</li> </ul> <p>Example: Relationship Inference (Diagram)<br>This example illustrates how relationships between characters are inferred based on events.</p> <ul> <li>Statement<br>Hermione Granger and Ron Weasley are involved in a romantic relationship. <ul> <li>Subject: romantic_love (dlp:relationship)</li> <li>Predicate: dlp:involves</li> <li>Object: Hermione Granger and Ron Weasley (gc:G1_Character)</li> <li>Inference Process</li> </ul> </li> <li>The inference-making process for this relationship statement includes: <ul> <li>Premise: The relationship is based on events. In this case, the premise is the event statement: "Hermione Granger and Ron Weasley participated in a kissing event during the Battle of Hogwarts."</li> <li>Method: This relationship was inferred based on ontology rules, specifically that the relationship forms based on events.</li> <li>Premise Belief: The premise belief (the kissing event) necessitates another inference process. This secondary inference process utilizes a textual source as evidence for the kissing event, which is a paragraph in the work "Harry Potter and the Deathly Hallows".</li> </ul> </li> <li>Premise Belief Inference <ul> <li>Event Statement: "Hermione Granger and Ron Weasley participated in a kissing event during the Battle of Hogwarts."</li> <li>Source: The source for this event is a crm:E73_Information_Object, potentially a text or metadata describing the event.</li> <li>Inference Method: This event is inferred through manual annotation of the text, which is linked via crminf:I3_Inference_Logic.</li> </ul> </li> </ul>
Usability Evaluation of the Agriculture Product Types Ontology (APTO)
<p><strong>Recommended citation</strong>:<br><br>Soares, F. M., Saraiva, A. M., Pires, L. F., Drucker, D. P., Braghetto, K. R., Santos, L. O. B. D. S., Moreira, D. D. A., Corrêa, F. E., & Delbem, A. C. B. (2025). A novel ux-based approach for ontology evaluation: Applying tree testing to the agricultural product types ontology. <em>IEEE Access</em>, 13, <a href="https://doi.org/10.1109/ACCESS.2025.3595447">https://doi.org/10.1109/ACCESS.2025.3595447</a><br><br>In evaluating the APTO ontology, we selected tree testing as the primary UX measuring protocol. We believe tree testing is particularly suitable for ontology evaluation as it combines various metrics, such as time on task and task success, to assess how users navigate and understand a hierarchy of concepts. This method allows us to trace user paths through the ontology's structure, identifying which aspects of the modeling may be confusing or inaccurate from the user's perspective. By analyzing these user interactions, we can gain valuable insights into how the ontology's design impacts usability, ultimately guiding improvements to better align with user needs.<br><br>Update in this version: images of pietrees.<br><br><br></p>
MIDAS MeSH to Ontology mappings
<p>These mapping files contain mappings from the RDF version of MeSH to the Geographical Entity Ontology and the NCBI Taxonomy for use in the Ontology Based Catalogue for Infection Disease Epidemiology.</p>
Processed proteomic and phosphoproteomic timeseries from Ostreococcus tauri, with Gene Ontology enrichment, from "A phospho-dawn of protein modification anticipates light onset in the picoeukaryote O. tauri"
<p>Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. These data tables in .XLSX format report partial proteome (Table_S1) and phosphoproteome data (Table_S2), assayed using shotgun mass-spectrometry, from cultures of the alga <em>Ostreococcus tauri </em>under light-dark cycles, sampled at Zeitgeber times (ZT, hours) 0, 4, 8, 12, 16 and 20. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Gene Ontology enrichment analysis was applied to infer the functional enrichment of the proteins or phosphoproteins, grouped by their loadings in PCA analysis (Table_S3), by hierarchical clustering (Table_S4) or by the peak time of their rhythmic profile (Table_S5).Prompted by night-peaking and apparently dark-stable proteins, we also tested the proteome of cultures transferred to prolonged darkness for 24, 48, 72 or 96h (Table_S6), where the proteome changed less than under the diel cycle. The raw data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909.</p>
SeaLiT Ontology - An extension of CIDOC-CRM for the modelling of Maritime History information
<p>The <strong>SeaLiT Ontology</strong> is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to <strong>maritime history</strong>. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information.</p> <p>The ontology uses and extends the <strong><a href="https://www.cidoc-crm.org/">CIDOC Conceptual Reference Model</a></strong> (ISO 21127:2014), in particular version 7.2.1, as a general ontology of human activity, things and events happening in space and time.</p> <p>The ontology has been developed following a bottom-up process from primary data collected in the context of the <a href="http://www.sealitproject.eu/"><strong>SeaLiT Project</strong></a> (<em>Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s</em>). SeaLiT is an international research project, funded by the ERC Starting Grant 2016, which explores the transition from sail to steam navigation and its effects on seafaring populations in the Mediterranean and the Black Sea between the 1850s and the 1920s.</p> <p>More information about the construction of the <strong>SeaLiT Ontology</strong>, the considered data sources, as well as their transformation to a knowledge graph using the SeaLiT Ontology, can be found in the following papers:</p> <blockquote> <p>P. Fafalios, A. Kritsotaki, and M. Doerr, "<em>The SeaLiT Ontology – An Extension of CIDOC-CRM for the Modeling and Integration of Maritime History Information"</em>. ACM Journal on Computing and Cultural Heritage, 2023. <a href="https://doi.org/10.1145/3586080">https://doi.org/10.1145/3586080</a> [<a href="https://arxiv.org/pdf/2301.04493.pdf">pdf</a>, <a href="https://users.ics.forth.gr/~fafalios/files/bibs/fafaliosSeaLiTOntology2023.bib">bib</a>]</p> </blockquote> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, and M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>]</p> </blockquote> <p>The (resolvable) <strong>namespace </strong>of the ontology is: <a href="http://www.sealitproject.eu/ontology/">http://www.sealitproject.eu/ontology/</a></p> <p>An <strong>OWL implementation</strong> of the ontology is available at: <a href="https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.2.owl">https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.2.owl</a></p> <p><strong>Knowledge graphs </strong>that make use of the SeaLiT Ontology are available at: <a href="https://zenodo.org/record/6460841">https://zenodo.org/record/6460841</a>. These RDF datasets integrate information of 16 different types of archival sources related to maritime history, including crew lists, payrolls, registers of different types, censuses, and employment records.</p>
A core ontology for modeling life cycle sustainability assessment on the Semantic Web with Accompanying Database
<p>To enable and support the uptake of semantic ontologies, we present a core ontology developed specifically to capture the data relevant for life cycle sustainability assessment. We further demonstrate the utility of the ontology by using it to integrate data relevant to sustainability assessments, such as EXIOBASE and the Yale Stocks and Flow Database to the Semantic Web. These datasets can be accessed by the machine-readable endpoint using SPARQL, a semantic query language.</p>
Mapping EDI, NEON and DataONE units to the QUDT ontology, 2022
In the metadata of digital environmental datasets, automated processing is hindered by the wide variety of representations for unit that may be human-readable, but may not be unambiguous or machine-interpretable, (e.g., grams per square meter, gm/m2, g/m2, gm-2, g/m^2, g.m-2, g m-2 and gramPerMeterSquared). Matching disparate representations of the same unit into a single unit concept from an ontology assists with interpretation and reuse by providing a linkage to a complete unit definitions with label, description, dimensions. Datasets with shared units can be identified during searches, and are more suitable for automating analyses and potential transformation. This dataset contains data and code associated with a project to map units in ecological metadata collected between 2013 and 2022 by DataONE, the Environmental Data Initiative and the U.S. National Ecological Observatory Network to the QUDT ontology using successive string transformations. Data entities include a) raw metadata as received (355,057 unit instances) b) integrated raw data c) substitution tables for string transformations d) resulting lookup table for 896 distinct units matched to QUDT units e) associated R code used for QUDT matching plus a web service and R functions for adding annotation elements to Ecological Metadata Language metadata documents. Using these substitutions and code, 91% of unit instances in the raw metadata could be matched to QUDT. Data and results are discussed in “Porter JH, M O’Brien, M Frants, S Earl, M Martin, C Laney. (in review) Using a Units Ontology to Annotate Pre-Existing Metadata. Submitted to Scientific Data.
FIG. 11. — A in The modern ontological natures of the Cairina moschata (Linnaeus, 1758) duck. Cases from Perú, the northern hemisphere, and digital communities
FIG. 11. — A, logo of D'Artagnan Foods Inc. Image via Wikimedia Commons; B, poster of campaign to stop the expansion of foie gras industry in China (https:// safarus.wordpress.com/2012/03/24/chinese-activists-call-for-boycott-of-the-largest-foie-gras-farm, last consultation: 06/09/2019).
MAOn: A Data-driven Methodology to Generate Living Ontologies
<p>This repository presents the MAnto Lite ontology created with our MAOn methodology in the context of transport and public and the accessibility it provides. Besides, a set of annotated data with the ontology as a validation method is presented. The MAOn methodology is characterized by being data-based, by creating live ontologies and by a thorough evaluation process of the created ontology.</p>
The Open Energy Ontology - Survey
<p>These files document the first survey conducted on the Open Energy Ontology. The analysis covers consistency of terms as well as term coverage.</p>
CORAL: A corpus of ontological requirements annotated with Lexico-Syntactic Patterns
<p>In this work we present CORAL (Corpus of Ontological Requirements Annotated with Lexico-syntactic patterns), an openly available corpus of 834 ontological requirements annotated and 29 lexico-syntactic patterns, from which 12 are proposed in this work. CORAL is openly available in three different open formats, namely, HTML, CSV and RDF.</p>
Epigraphic Ontology (draft)
<p>Stored version of the Epigraphic Ontology OWL files.</p> <p>Namespaces:</p> <p>The OWL Ontology is under development and will be namespaced <a href="https://w3id.org/epont/ontology">https://w3id.org/epont/ontology</a>. The included implementation of CRM-Tex as OWL Ontology is under development and will be redirected from the stable <a href="https://w3id.org/epont/crmtex">https://w3id.org/epont/crmtex</a> although the namespace used is already <a href="http://www.cidoc-crm.org/crmtex/">http://www.cidoc-crm.org/crmtex/</a>.</p> <p>GitHub repository for latest version: https://github.com/PietroLiuzzo/epiont</p> <p>See Modeling Document https://docs.google.com/document/d/1ju_a_JPllQUy3shjmC_mua8jaw7pPA3HjVkaQvnPyoY/edit# </p>
CROSSCULT upper-level ontology
<p>CC-Ontology is the upper-level ontology used in the CROSSCULT project, which also includes the concepts needed for representing venues, and the latest version of the vocabulary of terms.</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.