language en

The AI Model Ontology

Latest version:
https://www.w3id.org/iSeeOnto/aimodel
Contributors:
Anjana Wijekoon
Chamath Palihawadana
David Corsar
Ikechukwu Nkisi-Orji
Juan A. Recio-Garcia
Marta Caro Martínez
Imported Ontologies:
explanationPattern.owl
sio.owl
cpannotationschema.owl
prov-o#
eo
Download serialization:
RDF/XML
License:
http://insertlicenseURIhere.org
Visualization:
Visualize with WebVowl
Cite as:
The AI Model Ontology.
Ontology Specification Draft

Abstract

The focus of the AI Model ontology is to further expand the definition of the AI Task and AI Method concepts from the Explanation Ontology to be able to specify the information required by the iSee Platform. This ontology was created as part of the iSee project (https://isee4xai.com) which received funding from EPSRC under the grant number EP/V061755/1. iSee is part of the CHIST-ERA pathfinder programme for European coordinated research on future and emerging information and communication technologies.

Introduction back to ToC

Namespace declarations

Table 1: Namespaces used in the document
aimod<https://www.w3id.org/iSeeOnto/aimodel>
schema<http://schema.org>
void<http://rdfs.org/ns/void>
owl<http://www.w3.org/2002/07/owl>
resource<http://semanticscience.org/resource>
xsd<http://www.w3.org/2001/XMLSchema>
protege<http://protege.stanford.edu/plugins/owl/protege>
cpannotationschema<http://www.ontologydesignpatterns.org/schemas/cpannotationschema.owl>
skos<http://www.w3.org/2004/02/skos/core>
eo<https://purl.org/heals/eo>
rdfs<http://www.w3.org/2000/01/rdf-schema>
Term_frequency<http://www.w3id.org/iSeeOnto/aimodelTerm_frequency–>
cito<http://purl.org/spar/cito>
mlo<http://www.a2rd.net.br/mlo>
aimod<http://www.w3id.org/iSeeOnto/aimodel>
prov-o<http://www.w3.org/TR/prov-o>
rdf<http://www.w3.org/1999/02/22-rdf-syntax-ns>
terms<http://purl.org/dc/terms>
xml<http://www.w3.org/XML/1998/namespace>
vann<http://purl.org/vocab/vann>
obo<http://purl.obolibrary.org/obo>
prov<http://www.w3.org/ns/prov>
foaf<http://xmlns.com/foaf/0.1>
explainer<http://www.w3id.org/iSeeOnto/explainer>
dc<http://purl.org/dc/elements/1.1>

The AI Model Ontology: Overview back to ToC

This ontology has the following classes and properties.

Classes

Object Properties

Data Properties

The AI Model Ontology: Description back to ToC

Outline of the AI Model ontology main classes and relationships
Outline of the AI Model ontology main classes and relationships. Concepts highlighted in green are defined in this ontology.

Cross reference for The AI Model Ontology classes, properties and dataproperties back to ToC

This section provides details for each class and property defined by The AI Model Ontology.

Classes

AdaBoostc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#AdaBoost

has super-classes
Ensemble Method c

Agglomerative Clusteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Agglomerative_Clustering

is a "bottom-up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
Source
https://en.wikipedia.org/wiki/Hierarchical_clustering
has super-classes
Hierarchical Clustering c
is disjoint with
Divisive Clustering c

AI Task Goalc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#AITaskGoal

The goal of an AI Task, or the purpose it was developed to achieve
has super-classes
s i o 000337

Anomaly Detectionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#AnomalyDetection

identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well-defined notion of normal behaviour.
Source
https://en.wikipedia.org/wiki/Anomaly_detection
has super-classes
inductive task

Artificial Intelligence Modelc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#AIModel

An Artificial Intelligence Model that has been developed using some AI Method by training on some Dataset to solve some AI Task.
has super-classes

Audio Processingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Audio_Processing

has super-classes
inductive task

Autoencoderc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Autoencoder

is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”).
Source
https://en.wikipedia.org/wiki/Autoencoder
has super-classes
o m i t 0001486

Automatic Speech Recognitionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Automatic_Speech_Recognition

is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the main benefit of searchability.
Source
https://en.wikipedia.org/wiki/Speech_recognition
has super-classes
Computational Linguistics c

Autonomous Drivingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Autonomous_Driving

has super-classes
inductive task

Bayesian Case Modelc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Bayesian_Case_Model

has super-classes
Bayesian Method c

Bayesian Collaborative Filteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Bayesian_Collaborative_Filtering

has super-classes
Model based c

Bayesian Methodc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Bayesian_Method

has super-classes
o m i t 0001486
has sub-classes
Bayesian Case Model c

Bernouilli Naive Bayesc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Bernouilli_Naive_Bayes

has super-classes
Naive Bayes c

Binary Classificationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Binary_Classification

has super-classes
Classification c

Bootstrapped Aggregationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Bootstrapped_Aggregation

has super-classes
Ensemble Method c

Byte-Pair Encodingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Byte-Pair_Encoding

has super-classes
Tokenization c
is disjoint with
Dictionary Based Tokenization c, Regular Expression Tokenization c, Rule Based Tokenization c, White Space Tokenization c, WordPiece c

Case Based Reasoningc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Case_Based_Reasoning

is the process of solving new problems based on the solutions of similar past problems.
Source
https://en.wikipedia.org/wiki/Case-based_reasoning
has super-classes
Instance Based Learning c

Categorical Naive Bayesc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Categorical_Naive_Bayes

has super-classes
Naive Bayes c

Classificationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Classification

AI Tasks performing some classification.
has super-classes
inductive task
has sub-classes
Binary Classification c, Multi-class Classification c, Multi-label Classification c
is disjoint with
Forecasting c, abductive task, deductive task, inductive task

Clusteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Clustering

is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
Source
https://en.wikipedia.org/wiki/Cluster_analysis
has super-classes
inductive task, o m i t 0001486
has sub-classes
DBSCAN c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, K-Median c, Mean-Shift c, Spectral Bi-Clustering c, Spectral Co-Clustering c

Collaborative Filteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Collaborative_Filtering

has super-classes
Recommenders c
has sub-classes
Model based c, Neighbours based c

Complement Naive Bayesc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Complement_Naive_Bayes

has super-classes
Naive Bayes c

Computational Linguisticsc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Computational_Linguistic

is concerned with understanding written and spoken language from a computational perspective, and building artefacts that usefully process and produce language, either in bulk or in a dialogue setting.
Source
https://plato.stanford.edu/entries/computational-linguistics/
has super-classes
inductive task
has sub-classes
Automatic Speech Recognition c, Machine Translation c, Text Summarisation c

Computer Visionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Computer_Vision

deals with how computers can gain high-level understanding from digital images or videos.
Source
https://en.wikipedia.org/wiki/Computer_vision
has super-classes
inductive task
has sub-classes
Edge Detection c, Face Recognition c, Image Detection c, Image Generation c, Image Restoration c, Image Segmentation c, Object Tracking c, Video Motion Tracking c

Content-based Recommenderc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Content-based_Recommender

has super-classes
Recommenders c

Convolutional Neural Networkc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Convolutional_Neural_Network

has super-classes
o m i t 0017046
is disjoint with
Multi-layer Perceptron c, Recurrent Neural Network c, Transformers c

Datasetc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Dataset

A Dataset that has been used to train an AI model.
has super-classes

DBSCANc back to ToC or Class ToC

IRI: http://www.a2rd.net.br/mlo#DBSCAN

Density-based spatial clustering of applications with noise (DBSCAN) is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbours), marking as outliers points that lie alone in low-density regions (whose nearest neighbours are too far away).
Source
https://en.wikipedia.org/wiki/DBSCAN
has super-classes
Clustering c
is disjoint with
Mean-Shift c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, K-Median c, Spectral Bi-Clustering c, Spectral Co-Clustering c

Deep Belief Networkc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Deep_Belief_Network

is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.
Source
https://en.wikipedia.org/wiki/Deep_belief_network
has super-classes
o m i t 0001483

Deep GBT Mimic Techniquec back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Deep_GBT_Mimic_Technique

has super-classes
Mimic Learning c

Dictionary Based Tokenizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Dictionary_Based_Tokenization

has super-classes
Tokenization c
is disjoint with
Byte-Pair Encoding c, Regular Expression Tokenization c, Rule Based Tokenization c, White Space Tokenization c, WordPiece c

Dimensionality Reductionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Dimensionality_Reduction

is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.
Source
https://en.wikipedia.org/wiki/Dimensionality_reduction
has super-classes
artificial intelligence method
has sub-classes
Discrete Cosine Transformation c, Flexible Discriminant Analysis c, Fourier Transformation c, Linear Discriminant Analysis c, Mixture Discriminant Analysis c, Partial Least Squares Regression c, Principal Component Analysis c, Quadratic Discriminant Analysis c, Sammon Mapping c, t-SNE c

Discrete Cosine Transformationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Discrete_Cosine_Transformation

has super-classes
Dimensionality Reduction c

Divisive Clusteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Divisive_Clustering

is a "top-down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
Source
https://en.wikipedia.org/wiki/Hierarchical_clustering
has super-classes
Clustering c, Hierarchical Clustering c
is disjoint with
Agglomerative Clustering c, DBSCAN c, Mean-Shift c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, K-Median c, Spectral Bi-Clustering c, Spectral Co-Clustering c

Edge Detectionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Edge_Detection

has super-classes
Computer Vision c

Ensemble Methodc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Ensemble_Method

combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
Source
https://scikit-learn.org/stable/modules/ensemble.html
has super-classes
o m i t 0001483
has sub-classes
AdaBoost c, Bootstrapped Aggregation c, Gradient Boosting c, Gradient Tree Boosting c, Random Forest c, Stacked Generalization c, Voting Classifier c, Voting Regressor c, Weighted Average c

Evolutionary Algorithmc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Evolutionary_Algorithm

is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection.
Source
https://en.wikipedia.org/wiki/Evolutionary_algorithm
has super-classes
Metaheuristic c
has sub-classes
Genetic Algorithm c, Genetic Programming c

Expectation Maximisationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Expectation_Maximisation

has super-classes
Clustering c
is disjoint with
DBSCAN c, Mean-Shift c, Divisive Clustering c, Hierarchical Clustering c, K-Means c, K-Median c, Spectral Bi-Clustering c, Spectral Co-Clustering c

Face Recognitionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Face_Recognition

has super-classes
Computer Vision c

FastTextc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#FastText

is a word embedding library that allows one to create an unsupervised learning or supervised learning algorithm for obtaining vector representations for words.
Source
https://en.wikipedia.org/wiki/FastText
has super-classes
Vectorization c
is disjoint with
GloVe c, Word2vec c

Flexible Discriminant Analysisc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Flexible_Discriminant_Analysis

has super-classes
Dimensionality Reduction c

Forecastingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Forecasting

An AI forecasting task.
has super-classes
inductive task
is disjoint with
Classification c, abductive task, deductive task, inductive task

Fourier Transformationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Fourier_Transformation

has super-classes
Dimensionality Reduction c

Genetic Algorithmc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Genetic_Algorithm

is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).
Source
https://en.wikipedia.org/wiki/Genetic_algorithm
has super-classes
Evolutionary Algorithm c

Genetic Programmingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Genetic_Programming

is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.
Source
https://en.wikipedia.org/wiki/Genetic_programming
has super-classes
Evolutionary Algorithm c

GloVec back to ToC or Class ToC

IRI: http://www.a2rd.net.br/mlo#GloVe

coined from Global Vectors, is an unsupervised learning algorithm for obtaining vector representations for words.
Source
https://en.wikipedia.org/wiki/GloVe
has super-classes
Vectorization c
is disjoint with
FastText c, Word2vec c

Gradient Boostingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Gradient_Boosting

has super-classes
Ensemble Method c

Gradient Tree Boostingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Gradient_Tree_Boosting

has super-classes
Ensemble Method c

Hidden Markov Modelc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Hidden_Markov_Model

has super-classes
Markov Process Model c

Hierarchical Clusteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Hierarchical_Clustering

is a general family of clustering algorithms that build nested clusters by merging or splitting them successively. This hierarchy of clusters is represented as a tree (or dendrogram).
Source
https://scikit-learn.org/stable/modules/clustering.html
has super-classes
Clustering c
has sub-classes
Agglomerative Clustering c, Divisive Clustering c
is disjoint with
DBSCAN c, Mean-Shift c, Divisive Clustering c, Expectation Maximisation c, K-Means c, K-Median c, Spectral Bi-Clustering c, Spectral Co-Clustering c

Hierarchical Markov Modelc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Hierarchical_Markov_Model

has super-classes
Markov Process Model c

Hybrid Recommenderc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Hybrid_Recommender

has super-classes
Recommenders c

Image Detectionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Image_Detection

has super-classes
Computer Vision c

Image Generationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Image_Generation

has super-classes
Computer Vision c

Image Restorationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Image_Restoration

has super-classes
Computer Vision c

Image Segmentationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Image_Segmentation

has super-classes
Computer Vision c

Information Extractionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Information_Extraction

is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources.
Source
https://en.wikipedia.org/wiki/Information_extraction
has super-classes
o m i t 0010354
has sub-classes
Named-Entity Recognition c, POS Tagging c

Information Retrievalc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Information_Retrieval

is the process of obtaining information system resources that are relevant to an information need from a collection of those resources.
Source
https://en.wikipedia.org/wiki/Information_retrieval
has super-classes
inductive task
has sub-classes
Ranking c, Recommendation c

Instance Based Learningc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Instance_Based_Learning

is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory.
Source
https://en.wikipedia.org/wiki/Instance-based_learning
has super-classes
artificial intelligence method
has sub-classes
Case Based Reasoning c, k-Nearest Neighbour c

K-Meansc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#K-Means

is a vector quantisation method that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centres or cluster centroid), serving as a cluster prototype.
Source
https://en.wikipedia.org/wiki/K-means_clustering
has super-classes
Clustering c
has sub-classes
Mini-Batch K-Means c
is disjoint with
DBSCAN c, Mean-Shift c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Median c, Spectral Bi-Clustering c, Spectral Co-Clustering c

K-Medianc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#K-Median

is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median.
Source
https://en.wikipedia.org/wiki/K-medians_clustering
has super-classes
Clustering c
is disjoint with
DBSCAN c, Mean-Shift c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, Spectral Bi-Clustering c, Spectral Co-Clustering c

k-Nearest Neighbourc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#k-Nearest_Neighbour

has super-classes
Instance Based Learning c

k-Nearest Neighbourc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#k-Nearest_Neighbour_Recommender

has super-classes
Neighbours based c

K-Nearest Neighboursc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#K-Nearest_Neighbours

is a supervised machine learning algorithm for labeling an unknown data point given existing labeled data.
Source
https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
has super-classes
o m i t 0001483

Knowledge-based Recommenderc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Knowledge-based_Recommender

has super-classes
Recommenders c

Linear Discriminant Analysisc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Linear_Discriminant_Analysis

has super-classes
Dimensionality Reduction c

Linear Regressionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Linear_Regression

has super-classes
o m i t 0001483

Logistic Regressionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Logistic_Regression

is a statistical model that models the probability of one event (out of two alternatives) taking place by having the log-odds (the logarithm of the odds) for the event be a linear combination of one or more independent variables ("predictors").
Source
https://en.wikipedia.org/wiki/Logistic_regression
has super-classes
o m i t 0001483

Machine Translationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Machine_Translation

is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
Source
https://en.wikipedia.org/wiki/Machine_translation
has super-classes
Computational Linguistics c

Markov Chainc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Markov_Chain

has super-classes
Markov Process Model c

Markov Decision Processc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Markov_Decision_Process

has super-classes
Markov Process Model c

Markov Process Modelc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Markov_Process_Model

has super-classes
artificial intelligence method
has sub-classes
Hidden Markov Model c, Hierarchical Markov Model c, Markov Chain c, Markov Decision Process c, Markov Random Field c, Tolerant Markov Model c

Markov Random Fieldc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Markov_Random_Field

has super-classes
Markov Process Model c

Matrix Factorisationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Matrix_Factorisation

has super-classes
Model based c

Mean-Shiftc back to ToC or Class ToC

IRI: http://www.a2rd.net.br/mlo#Mean-Shift

has super-classes
Clustering c
is disjoint with
DBSCAN c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, K-Median c, Spectral Bi-Clustering c, Spectral Co-Clustering c

Metaheuristicc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Metaheuristic

is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.
Source
https://en.wikipedia.org/wiki/Metaheuristic
has super-classes
o m i t 0001177
has sub-classes
Evolutionary Algorithm c

Mimic Learningc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Mimic_Learning

has super-classes
o m i t 0001483
has sub-classes
Deep GBT Mimic Technique c

Mini-Batch K-Meansc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Mini-Batch_K-Means

has super-classes
K-Means c

Mixture Discriminant Analysisc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Mixture_Discriminant_Analysis

has super-classes
Dimensionality Reduction c

Model basedc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Model_based

has super-classes
Collaborative Filtering c
has sub-classes
Bayesian Collaborative Filtering c, Matrix Factorisation c
is disjoint with
Neighbours based c

Multi-class Classificationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Multi-class_Classification

has super-classes
Classification c

Multi-label Classificationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Multi-label_Classification

has super-classes
Classification c

Multi-layer Perceptronc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Multi-layer_Perceptron

has super-classes
o m i t 0017046
is disjoint with
Convolutional Neural Network c, Recurrent Neural Network c, Transformers c

Multinomial Naive Bayesc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Multinomial_Naive_Bayes

has super-classes
Naive Bayes c

Naive Bayesc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Naive_Bayes

are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.
Source
https://scikit-learn.org/stable/modules/naive_bayes.html
has super-classes
o m i t 0001483
has sub-classes
Bernouilli Naive Bayes c, Categorical Naive Bayes c, Complement Naive Bayes c, Multinomial Naive Bayes c

Named-Entity Recognitionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Named_Entity_Recognition

is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Source
https://en.wikipedia.org/wiki/Named-entity_recognition
has super-classes
Information Extraction c

Neighbours basedc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Neighbours_based

has super-classes
Collaborative Filtering c
has sub-classes
k-Nearest Neighbour c
is disjoint with
Model based c

Object Trackingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Object_Tracking

has super-classes
Computer Vision c

Optimisationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Optimisation

is the problem of finding the best solution from all feasible solutions.
Source
https://en.wikipedia.org/wiki/Optimization_problem
has super-classes
inductive task

Partial Least Squares Regressionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Partial_Least_Squares_Regression

has super-classes
Dimensionality Reduction c

Policy-based Reinforcement Learningc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Policy-based_Reinforcement_Learning

has super-classes
Reinforcement Learning c
is disjoint with
Q-Learning c, SARSA c, Temporal Difference c

POS Taggingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#POS_Tagging

Part-of-Speech Tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
Source
https://en.wikipedia.org/wiki/Part-of-speech_tagging
has super-classes
Information Extraction c

Principal Component Analysisc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Principal_Component_Analysis

has super-classes
Dimensionality Reduction c

Q-Learningc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Q-Learning

has super-classes
Reinforcement Learning c
is disjoint with
Policy-based Reinforcement Learning c, SARSA c, Temporal Difference c

Quadratic Discriminant Analysisc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Quadratic_Discriminant_Analysis

has super-classes
Dimensionality Reduction c

Random Forestc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Random_Forest

has super-classes
Ensemble Method c

Rankingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Ranking

has super-classes
Information Retrieval c

Recommendationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Recommendation

has super-classes
Information Retrieval c

Recommendersc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Recommenders

is a subclass of information filtering systems that seek to predict the "rating" or "preference" a user would give to an item.
Source
https://en.wikipedia.org/wiki/Recommender_system
has super-classes
artificial intelligence method
has sub-classes
Collaborative Filtering c, Content-based Recommender c, Hybrid Recommender c, Knowledge-based Recommender c

Recurrent Neural Networkc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Recurrent_Neural_Network

has super-classes
o m i t 0017046
is disjoint with
Convolutional Neural Network c, Multi-layer Perceptron c, Transformers c

Regressionc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Regression

has super-classes
inductive task

Regular Expression Tokenizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Regular_Expression_Tokenization

has super-classes
Tokenization c
is disjoint with
Byte-Pair Encoding c, Dictionary Based Tokenization c, Rule Based Tokenization c, White Space Tokenization c, WordPiece c

Reinforcement Learningc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Reinforcement_Learning

is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximise the notion of cumulative reward.
Source
https://en.wikipedia.org/wiki/Reinforcement_learning
has super-classes
o m i t 0001480
has sub-classes
Policy-based Reinforcement Learning c, Q-Learning c, SARSA c, Temporal Difference c

Restricted Boltzmann Machinec back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Restricted_Boltzmann_Machine

is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
Source
https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine
has super-classes
o m i t 0001483

Rule Basedc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Rule_Based

has super-classes
deductive task

Rule Based Tokenizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Rule_Based_Tokenization

has super-classes
Tokenization c
is disjoint with
Byte-Pair Encoding c, Dictionary Based Tokenization c, Regular Expression Tokenization c, White Space Tokenization c, WordPiece c

Sammon Mappingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Sammon_Mapping

has super-classes
Dimensionality Reduction c

SARSAc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#SARSA

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning.
Source
https://en.wikipedia.org/wiki/State-action-reward-state-action
has super-classes
Reinforcement Learning c
is disjoint with
Policy-based Reinforcement Learning c, Q-Learning c, Temporal Difference c

Self-organizing Mapc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Self-organizing_Map

is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data.
Source
https://en.wikipedia.org/wiki/Self-organizing_map
has super-classes
o m i t 0001486

Signal Processingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Signal_Processing

focuses on analysing, modifying, and synthesizing signals such as sound, images, and scientific measurements to improve transmission, storage efficiency and subjective quality and to also emphasize or detect components of interest in a measured signal.
Source
https://en.wikipedia.org/wiki/Signal_processing
has super-classes
inductive task

Spectral Bi-Clusteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Spectral_Bi-Clustering

has super-classes
Clustering c
is disjoint with
DBSCAN c, Mean-Shift c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, K-Median c, Spectral Co-Clustering c

Spectral Co-Clusteringc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Spectral_Co-Clustering

has super-classes
Clustering c
is disjoint with
DBSCAN c, Mean-Shift c, Divisive Clustering c, Expectation Maximisation c, Hierarchical Clustering c, K-Means c, K-Median c, Spectral Bi-Clustering c

Stacked Generalizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Stacked_Generalization

has super-classes
Ensemble Method c

t-SNEc back to ToC or Class ToC

IRI: http://www.a2rd.net.br/mlo#t-SNE

t-distributed stochastic neighbour embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
has super-classes
Dimensionality Reduction c

Temporal Differencec back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Temporal_Difference

a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function.
Source
https://en.wikipedia.org/wiki/Temporal_difference_learning
has super-classes
Reinforcement Learning c
is disjoint with
Policy-based Reinforcement Learning c, Q-Learning c, SARSA c

Term frequency–Inverse document frequencyc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Term_frequency–Inverse_document_frequency

is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.
Source
https://en.wikipedia.org/wiki/Tf-idf
has super-classes
Vectorization c

Text Summarisationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Text_Summarisation

reduces the number of sentences and words of a document without changing its meaning.
has super-classes
Computational Linguistics c

Tokenizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Tokenization

is the process of demarcating and possibly classifying sections of a string of input characters.
Source
https://en.wikipedia.org/wiki/Lexical_analysis#Tokenization
has super-classes
o m i t 0010354
has sub-classes
Byte-Pair Encoding c, Dictionary Based Tokenization c, Regular Expression Tokenization c, Rule Based Tokenization c, White Space Tokenization c, WordPiece c

Tolerant Markov Modelc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Tolerant_Markov_Model

has super-classes
Markov Process Model c

Transformersc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Transformers

has super-classes
o m i t 0017046
is disjoint with
Convolutional Neural Network c, Multi-layer Perceptron c, Recurrent Neural Network c

Vectorizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Vectorization

is a methodology in NLP to map words or phrases from vocabulary to a corresponding vector of real numbers.
has super-classes
o m i t 0010354
has sub-classes
FastText c, GloVe c, Term frequency–Inverse document frequency c, Word2vec c

Video Motion Trackingc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Video_Motion_Tracking

has super-classes
Computer Vision c

Voting Classifierc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Voting_Classifier

has super-classes
Ensemble Method c

Voting Regressorc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Voting_Regressor

has super-classes
Ensemble Method c

Weighted Averagec back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Weighted_Average

has super-classes
Ensemble Method c

White Space Tokenizationc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#White_Space_Tokenization

has super-classes
Tokenization c
is disjoint with
Byte-Pair Encoding c, Dictionary Based Tokenization c, Regular Expression Tokenization c, Rule Based Tokenization c, WordPiece c

Word2vecc back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#Word2vec

is a technique for natural language processing that uses a neural network model to learn word associations from a large corpus of text.
Source
https://en.wikipedia.org/wiki/Word2vec
has super-classes
Vectorization c
is disjoint with
GloVe c, FastText c

WordPiecec back to ToC or Class ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#WordPiece

has super-classes
Tokenization c
is disjoint with
Byte-Pair Encoding c, Dictionary Based Tokenization c, Regular Expression Tokenization c, Rule Based Tokenization c, White Space Tokenization c

Object Properties

has data typeop back to ToC or Object Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#hasDataType

The relationship between a DataSet and the type of data it contains.

has dataset typeop back to ToC or Object Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#hasDatasetType

Defines the types of the dataset

has feature typeop back to ToC or Object Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#hasFeatureType

Relationship between a DataSet and the type of features it contains.
has super-properties
top object property

solvesop back to ToC or Object Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#solves

The relationship between an AI Model and the AI Task it (attempts to) solve.

trained onop back to ToC or Object Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#trainedOn

Relationship between an AI Method, and the types of data it was trained on.

Data Properties

number of featuresdp back to ToC or Data Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#numberOfFeatures

The number of features a dataset has.

number of instancesdp back to ToC or Data Property ToC

IRI: http://www.w3id.org/iSeeOnto/aimodel#numberOfInstances

The number of instances in a dataset.

Legend back to ToC

c: Classes
op: Object Properties
dp: Data Properties
ni: Named Individuals

References back to ToC

Add your references here. It is recommended to have them as a list.

Acknowledgments back to ToC

The authors would like to thank Silvio Peroni for developing LODE, a Live OWL Documentation Environment, which is used for representing the Cross Referencing Section of this document and Daniel Garijo for developing Widoco, the program used to create the template used in this documentation.