Ontotext

AUGUST 15, 2019

In order to feel comfortable and keep up with the training, participants need to have at least a basic understanding of the SPARQL query language and the underlying graph-based data model. Therefore, we provide a theoretical overview of both, including some practical exercises in SPARQL. Want to see for yourselves?

Knowledge Discovery 70

Knowledge Discovery Technology Metadata Interactive 70

Ontotext

APRIL 6, 2023

Knowledge graphs are changing the game A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data. standards modeled in a knowledge graph!

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

The Unofficial Google Data Science Blog

JULY 22, 2020

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. In practice, one may want to use more complex models to make these estimates.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. References. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Banko, M., & Brill, E. 30(2–3), 195–215. link] Ling, C. X., & Li, C.

Machine Learning 136

Machine Learning Metrics Data mining Data Science 136

Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. References. [1]

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Ontotext

AUGUST 22, 2023

As a result it turns them into the type of data that can be managed programmatically while containing all the agreed upon meanings for human reference. Semantically integrated data makes metadata meaningful, allowing for better interpretation, improved search, and enhanced knowledge-discovery processes.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Ontotext

AUGUST 16, 2019

In order to feel comfortable and keep up with the training, participants need to have at least a basic understanding of the SPARQL query language and the underlying graph-based data model. Therefore, we provide a theoretical overview of both, including some practical exercises in SPARQL. Want to see for yourselves?

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

Ontotext

AUGUST 10, 2023

And, the added value of SIM cards and knowledge graphs would be much smaller without the internet, particularly WWW – the global information space that facilitates communication. It makes it possible to exchange references, web page URLs, videos, and pictures, instead of having to send the actual content point to point.

Technology 52

Technology Knowledge Discovery Interactive Enterprise 52

Ontotext

OCTOBER 28, 2021

However, although some ontologies or domain models are available in RDF/OWL, many of the original datasets that we have integrated into Ontotext’s Life Sciences and Healthcare Data Inventory are not. Although there are already established reference datasets in some domains (e.g. Visual Ontology Modeling With metaphactory.

Visualization 52

Visualization Interactive Knowledge Discovery Dashboards 52

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data. The statistical effect size is often defined as [ e=frac{delta}{sigma} ]which is the difference in group means as a fraction of the (pooled) standard deviation (sometimes referred to as “Cohen’s d” ).

Experimentation 42

Experimentation Statistics Metrics Measurement 42

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. For more on ad CTR estimation, refer to [2].

KDD 40

KDD Testing Machine Learning Measurement 40

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

At Google, we tend to refer to them as slices. Rare binary event example In the previous post , we discussed how rare binary events can be fundamental to the LSOS business model. In statistics, such segments are often called “blocks” or “strata”. To that end, it is worth studying them in more detail. A burden has been lifted.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Ontotext

FEBRUARY 21, 2024

RAG and Ontotext offerings: a perfect synergy RAG is an approach for enhancing an existing large language model (LLM) with external information provided as part of the input prompt, or grounding context. So we have built a dataset using schema.org to model and structure this content into a knowledge graph. answer { { select ?question

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

Ontotext

AUGUST 31, 2023

The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge.

Modeling 64

Modeling Knowledge Discovery Optimization Enterprise 64

Ontotext

NOVEMBER 11, 2024

Knowledge Graphs Defined and Why Semantics (and Ontologies) Matter According to Wikipedia , a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. The RDF-star extension makes it easy to model provenance and other structured metadata.

Metadata 52

Metadata Knowledge Discovery Data Integration Management 52