Machine Learning Schema

IRI:
http://www.w3.org/ns/mls#
Version IRI:
http://www.w3.org/2016/03/mls#
Other visualisation:
Ontology source

Table of Content

  1. Classes
  2. Object Properties
  3. Annotation Properties
  4. Namespace Declarations

Classes

algorithmc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Algorithm

The algorithm regardless software implementation.
has super-classes
information entityc

datac back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Data

Data is a data item composed of data examples and it may be of a various level of granularity and complexity. With regard to granularity, it can be a whole dataset (for instance, one main table and possibly other tables), or only a single table, or only a feature (e.g., a column of a table), or only an instance (e.g., row of a table), or a single feature-value pair. With regard to complexity, data examples are characterized by their datatype, which may be arbitrarily complex (e.g., instead of a table it can be an arbitrary graph).
has super-classes
information entityc
has qualityop some data characteristicc
has sub-classes
datasetc, featurec

data characteristicc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#DataCharacteristic

DataCharacteristic is a distinguishing quality or property that distinguish one data from another. Such characteristics are often statistical ones (e.g., the number of instances or the number of features of a data set). They may be also informationtheoretic measures (e.g., class entropy of a categorical data set) or geometric measures of data complexity (e.g., the highest discriminatory power of any single feature in the data set).
has super-classes
qualityc
has sub-classes
dataset characteristicc, feature characteristicc

datasetc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Dataset

has super-classes
datac

dataset characteristicc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#DatasetCharacteristic

has super-classes
data characteristicc

evaluation measurec back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#EvaluationMeasure

EvaluationMeasure is a measure to assess the performance of the model generated by the process that realizes the task. Examples are predictive accuracy or f-measure.
has super-classes
information entityc

evaluation procedurec back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#EvaluationProcedure

EvaluationProcedure is a technique to evaluate machine learning models. Examples are cross-validation and leave-one-out.
has super-classes
information entityc

evaluation specificationc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#EvaluationSpecification

EvaluationProcedure is a technique to evaluate machine learning models. Examples are cross-validation and leave-one-out.
has super-classes
information entityc
hasPartop some evaluation measurec
definesop some taskc
hasPartop some evaluation procedurec

experimentc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Experiment

Experiment is a collection of runs.
has super-classes
Processc
hasPartop some runc

featurec back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Feature

has super-classes
datac

feature characteristicc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#FeatureCharacteristic

has super-classes
data characteristicc

hyper parameterc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#HyperParameter

Hyperparameter is a prior parameter of an implementation, i.e., a parameter which is set before its execution (e.g. C, the complexity parameter, in weka.SMO).
has super-classes
information entityc

hyper parameter settingc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#HyperParameterSetting

HyperParameterSetting is an entity which connects a hyperparameter and its value that is being set before an implementation execution.
has super-classes
information entityc
specified byop some hyper parameterc
has valuedp some literal

implementationc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Implementation

Implementation is an executable implementation of a machine learning algorithm, a script, or a workflow. It is versioned, and sometimes belongs to a library (e.g. WEKA).
has super-classes
information entityc
implementsop some algorithmc
has qualityop some implementation characteristicc
hasHyperParameterop some hyper parameterc

implementation characteristicc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#ImplementationCharacteristic

ImplementationCharacteristic is a distinguishing quality or property that distinguish one implementation from another.
has super-classes
qualityc

modelc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Model

Model is a generalization of a set of training data able to predict values for unseen instances. It is an output from an execution of a data mining algorithm implementation. Models have a dual nature. They can be treated as data structures and as such represented, stored and manipulated. On the other hand, they act as functions and are executed, taking as input data examples and giving as output the result of applying the function to a data example. Models can also be divided into global or local ones. A global model has global coverage of a data set, i.e., it generalizes the whole data set. A local model, such as a pattern set, is a set of local hypotheses, i.e. each applies to a limited region of the data set.
has super-classes
information entityc
has qualityop some model characteristicc

model characteristicc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#ModelCharacteristic

ModelCharacteristic is a distinguishing quality or property that distinguish one model from another. An example model characetristic may be interpretabilty or a complexity of the model.
has super-classes
qualityc

model evaluationc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#ModelEvaluation

ModelEvaluation is a setting of a value of the performance measure specified by the evaluation specification. It connects a measure specification with its value.
has super-classes
information entityc
specified byop some evaluation measurec
has valuedp some literal

Processc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Process

has sub-classes
experimentc, runc, studyc

runc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Run

Run is an execution of an implementation on a machine (computer). It is limited in time (has a start and end point), can be successful or failed.
has super-classes
Processc
hasInputop some hyper parameter settingc
realizesop some algorithmc
has outputop some modelc
executesop some implementationc
achievesop some taskc
has outputop some model evaluationc
hasInputop some datac

softwarec back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Software

Software is implemented computer programs, procedures, scripts or rules with associated documentation, possibly constituting an organized environment, stored in read/write memory for the purpose of being executed within a computer system.
has super-classes
information entityc
hasPartop some implementationc

studyc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Study

Study is a collection of runs that belong together to do some kind of analysis on its results. This analysis can be general or very specific (e.g. a hypothesis test). Can be linked to files, data, that belong to it.
has super-classes
Processc
hasPartop some experimentc

taskc back to ToC or Class ToC

IRI: http://www.w3.org/ns/mls#Task

Task is a formal description of a process that needs to be completed (e.g. based on inputs and outputs). A Task is any piece of work that needs to be addressed in the data mining process. In ML Schema, it is defined based on data.
has super-classes
information entityc
definedOnop some datac

Object Properties

achievesop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#achieves

A relation between a run and a task, where the run achieves specifications formulated by the task.

definedOnop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#definedOn

A relation between a task and either the data or an evaluation specification pertinent to this task.
has super-properties
top object property
is inverse of
definesop

definesop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#defines

is inverse of
definedOnop

executesop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#executes

A relation between a run and an implemantation that is being executed during the run.
has super-properties
top object property

has outputop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#hasOutput

A relation between a run and either a model or model evaluation that is produced on it’s output.

has qualityop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#hasQuality

A relation between entities and their various characteristics.

hasHyperParameterop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#hasHyperParameter

A relation between an implementation of a machine learning algorithm and its hyperparameter.
has super-properties
top object property

hasInputop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#hasInput

A relation between a run and data that is taken as input to the run.
has super-properties
top object property

hasPartop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#hasPart

A relation which represents a part-whole relationship holding between an entity and its part.

implementsop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#implements

A relation between an information entity and a specification that it conforms to.

realizesop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#realizes

A relation between a run and an algorithm, where the run realizes specifications formulated by the algorithm.
has super-properties
top object property

specified byop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/mls#specifiedBy

A relation between an entity and the information content entity that specifies it.

Annotation Properties

descriptionap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/description

has versionap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/hasVersion

issuedap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/issued

modifiedap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/modified

noteap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#note

publisherap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/publisher

titleap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/title

Namespace Declarations back to ToC

default namespace
http://www.w3.org/ns/mls#
1-1
http://purl.org/dc/elements/1.1/
aboutdcmi
http://purl.org/dc/aboutdcmi#
owl
http://www.w3.org/2002/07/owl#
protege
http://protege.stanford.edu/plugins/owl/protege#
rdf
http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs
http://www.w3.org/2000/01/rdf-schema#
rfc
http://www.ietf.org/rfc/
skos
http://www.w3.org/2004/02/skos/core#
terms
http://purl.org/dc/terms/
xsd
http://www.w3.org/2001/XMLSchema#

This HTML document was obtained by processing the OWL ontology source code through LODE, Live OWL Documentation Environment, developed by Silvio Peroni.