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

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 Modeling 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 41

Experimentation Statistics Metrics Measurement 41

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. Exascale computing refers to systems capable of at least one exaFLOPS calculation per second and that is billion billion (or if you wish a quintillion) operations per second.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. We can’t imagine looking at the Semantic Web as an artifact.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

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 95

Experimentation Statistics Testing Strategy 95

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

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 Modeling 40

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 Knowledge Discovery 136

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

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

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 40

Experimentation Metrics Statistics Measurement 40

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 Optimization Knowledge Discovery Enterprise 64