Ontotext

APRIL 6, 2023

There is an overwhelming amount of standardization efforts and reference initiatives, which double down on the benefit from the knowledge graph approach. standards modeled in a knowledge graph! Additionally, many organizations and corporations are pushing for the adoption of Industry 4.0

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

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

FineReport

DECEMBER 19, 2019

Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen. For super rookies, the first task is to understand what data analysis is.

Knowledge Discovery 98

Knowledge Discovery Visualization Reporting Experimentation 98

Ontotext

DECEMBER 30, 2019

As 2019 comes to an end, we at Ontotext are taking stock of the most fascinating things we have done to empower knowledge management and knowledge discovery this year. In 2019, Ontotext open-sourced the front-end and engine plugins of GraphDB to make the development and operation of knowledge graphs easier and richer.

Knowledge Discovery 52

Knowledge Discovery Publishing Technology Enterprise 52

Ontotext

AUGUST 15, 2019

Still, newcomers are advised to dedicate some time to any of the excellent SPARQL tutorials out there, some of which are referred to in the FAQ section of the training page. Therefore, we provide a theoretical overview of both, including some practical exercises in SPARQL.

Knowledge Discovery 70

Knowledge Discovery Technology Metadata Interactive 70

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The former is a term used for models where the data has been labeled, whereas, unsupervised learning, on the other hand, refers to unlabeled data. Classification.

Data mining 77

Data mining KDD Data Science Knowledge Discovery 77

Ontotext

AUGUST 16, 2019

Still, newcomers are advised to dedicate some time to any of the excellent SPARQL tutorials out there, some of which are referred to in the FAQ section of the training page. Therefore, we provide a theoretical overview of both, including some practical exercises in SPARQL.

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

Domino Data Lab

DECEMBER 4, 2020

The openness of the Domino Data Science platform allows us to use any language, tool, and framework while providing reproducibility, compute elasticity, knowledge discovery, and governance. References. [1] In this tutorial, we demonstrated how to carry out a simple Non-Compartmental Analysis. 1] Gabrielsson J, Weiner D.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

In this blog post, we summarize that paper and refer you to it for details. References [1] Henning Hohnhold, Deirdre O'Brien, Diane Tang, Focus on the Long-Term: It's better for Users and Business , Proceedings 21st Conference on Knowledge Discovery and Data Mining, 2015. [2] 2] Ron Kohavi, Randal M.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Ontotext

OCTOBER 28, 2021

Although there are already established reference datasets in some domains (e.g. UniProt for proteomics, ENSEMBL for genomics, ChEMBL for bioactive chemicals, etc) still the semantic harmonization of the data into a knowledge graph remains a significant challenge.

Visualization 52

Visualization Interactive Knowledge Discovery Dashboards 52

Domino Data Lab

MAY 20, 2021

References. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. The dataset and code used in this blog post are available at [link] and all results shown here are fully reproducible, thanks to the Domino reproducibility engine, which is part of the Domino Data Science platform.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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. References [1] Kohavi, Ron, Randal M. Henne, and Dan Sommerfield. 2] Scott, Steven L. 2015): 37-45. [3]

Experimentation 95

Experimentation Statistics Testing Knowledge Discovery 95

Ontotext

FEBRUARY 21, 2024

We have our data indexed in the vector database and we want to answer our targeted question “ What are some common applications of knowledge graphs? “. The post Enhancing Knowledge Discovery: Implementing Retrieval Augmented Generation with Ontotext Technologies appeared first on Ontotext. answer { { select ?question

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

The Unofficial Google Data Science Blog

JANUARY 14, 2016

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” ). Further assume $Y_i sim N(mu,sigma^2)$ under control and $Y_i sim N(mu+delta,sigma^2)$ under treatment (i.e. known, equal variances).

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

For more on ad CTR estimation, refer to [2]. References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2] A machine learning system produces an estimated CTR $t_i$ for each query-ad pair. Our method has four steps: Bin by $t$.

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. References [1] Diane Tang, Ashish Agarwal, Deirdre O’Brien, Mike Meyer, “ Overlapping Experiment Infrastructure: More, Better, Faster Experimentation ”, Proceedings 16th Conference on Knowledge Discovery and Data Mining, Washington, DC A burden has been lifted.

Experimentation 40

Experimentation Statistics Metrics Measurement 40

Domino Data Lab

AUGUST 1, 2021

References. Conference on Knowledge Discovery and Data Mining, pp. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. Maria Fox, Derek Long, and Daniele Magazzeni. Explainable planning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Ontotext

AUGUST 31, 2023

What makes a knowledge graph a unique and powerful data solution is the semantic (data) model, or ontology , that is part of it. We use the terms semantic model, semantic data model and ontology interchangeably to refer to formal and explicit definitions of the concepts and relations within a domain.

Modeling 64

Modeling Knowledge Discovery Optimization Enterprise 64