Linked Open Vocabularies

https://w3id.org/ishikawa-diagram-ontology

The Ishikawa ontology aims to provide a data and view model to manage data encoded in Ishikawa diagrams which are also known as fishbone or cause and effect diagram (CED). Ishikawa diagrams result from (iterative) workshops. Thus, the ontology includes the basic modelling of workshops to create Ishikawa diagrams. @en

ldvm- Vocabulary for Linked Data Visualization Model

http://linked.opendata.cz/ontology/ldvm/

Vocabulary for Linked Data Visualization Model (LDVM) serves for description and configuration of components and pipelines according to LDVM @en

limo- Linked Statistical Models Vocabulary

http://purl.org/limo-ontology/limo/

A Vocabulary for Incorporating Predictive Models into the Linked Data Web @en

loin- Level of Information Need (LOIN) Ontology

https://w3id.org/loin

The Level of Information Need (LOIN) Ontology is defined for specifying information requirements for delivery of data in a buildings' life cycle. The LOIN ontology is based on the standard BS EN 17412-1 (2020). Furthermore, it is extended with vocabulary for connecing Information Delivery Specifications (IDS) and Information containers for linked document delivery (ICDD) as per ISO 21597-1 (2020). @en

lsc- Linked Science Core Vocabulary

http://linkedscience.org/lsc/ns#

LSC, the Linked Science Core Vocabulary, is a lightweight vocabulary providing terms to enable publishers and researchers to relate things in science to time, space, and themes. @en

tribont-material- Material module

https://w3id.org/tribont/material

The goal of this module is to represent the materials that can be involved in the tribological experiments as part of the tested samples. @en

mexalgo- MEX Algorithm Ontology

http://mex.aksw.org/mex-algo

MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-algo layer represents the algorithm information existing into a basic machine learning experiment. @en

mexcore- MEX Core Vocabulary

http://mex.aksw.org/mex-core

MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-core layer represents the core information gathered from a basic machine learning experiment design. @en

mexperf- MEX Performance Ontology

http://mex.aksw.org/mex-perf

MEX is an RDF vocabulary designed to facilitate interoperability between published machine learning experiments results on the Web. The mex-perf layer is the 3rd level of the MEX for representing the machine learning algorithm's performances. @en

mls- Machine Learning Schema

http://www.w3.org/ns/mls

ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments @en

msr- Measurement Ontology

http://www.telegraphis.net/ontology/measurement/measurement#

The Measurement Ontology is an ontology in which measurements may be rendered @en

nno- The Neural Network Ontology

https://w3id.org/nno/ontology

This is the Neural Network Ontology. Designed by the AIFB (http://www.aifb.kit.edu/web/Web_Science) @en

np- Nano publication ontology

http://www.nanopub.org/nschema

The nanopub ontology @en

nrv- Normative Requirements Vocabulary

http://ns.inria.fr/nrv

An OWL vocabulary for describing normative requirements. @en