Ontology learning (ontology extraction,ontology augmentation generation, ontology generation, or ontology acquisition) is the automatic or semi-automatic...

In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the...

databases into RDF, identity resolution, knowledge discovery and ontology learning. The general process uses traditional methods from information extraction...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

Ontology (information science) Ontology components Ontology double articulation Ontology learning Ontology modularization Semantic decision table Semantic...

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...

International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

(2024-05-26). NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning (PDF). Extended Semantic Web Conference 2024. Hersonissos, Greece...

token. After this step, the model was then fine-tuned with reinforcement learning feedback from humans and AI for human alignment and policy compliance.: 2 ...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

of RDF ontologies. Several tools like the graphical ontology editor OIModeler or the KAON Server were based on KAON. There are ontology learning companion...

The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was...

term "softargmax", though the term "softmax" is conventional in machine learning. This section uses the term "softargmax" for clarity. Formally, instead...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether...

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year....

May 2024). NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning (PDF). Extended Semantic Web Conference 2024. Hersonissos, Greece...

In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

techniques. This normalised form distance is often used within many deep learning algorithms. In biology, there is a similar concept known as the Otsuka–Ochiai...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

natural language processing by machines. It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able...

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable...

information science, an upper ontology (also known as a top-level ontology, upper model, or foundation ontology) is an ontology (in the sense used in information...

Attention is a machine learning method that determines the importance of each component in a sequence relative to the other components in that sequence...

purposes. In addition, ontologies can be automatically updated in the crawling process. Dong et al. introduced such an ontology-learning-based crawler using...