Ontotext

AUGUST 22, 2023

Semantically integrated data makes metadata meaningful, allowing for better interpretation, improved search, and enhanced knowledge-discovery processes. And just like business models vary, semantic metadata projects have their unique characteristics.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Ontotext

MARCH 8, 2023

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

DECEMBER 1, 2023

Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises. from Q&A with Tim Berners-Lee ) Finally, Sumit highlighted the importance of knowledge graphs to advance semantic data architecture models that allow unified data access and empower flexible data integration.

Metadata 78

Metadata Sales Consulting Enterprise 78

Smart Data Collective

MARCH 13, 2020

Phase 4: Knowledge Discovery. Finally, models are developed to explain the data. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner. Algorithms can also be tested to come up with ideal outcomes and possibilities.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

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

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 Experimentation Reporting 98

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]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Ontotext

SEPTEMBER 15, 2020

There must be a representation of the low-level technical and operational metadata as well as the ‘real world’ metadata of the business model or ontologies. Create a human AND machine-meaningful data model. Formalize your data model using standards like RDF Schema and OWL. Integrate data with ETL or virtualization.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

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 models created using these algorithms could be evaluated against appropriate metrics to verify the model’s credibility. Data Mining Models. Classification.

Data mining 77

Data mining KDD Data Science Forecasting 77

Ontotext

AUGUST 19, 2020

XML and later JSON were the languages that enabled data interchange by establishing a common data model by establishing a standard description of the data being shared. Beyond the ability to ensure there was an enterprise wide data model, it was also possible to reuse data but with different metadata and schema.

Metadata 59

Metadata Knowledge Discovery Cost-Benefit Data Governance 59

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. Visual Ontology Modeling With metaphactory. This makes it much easier to collaborate and discuss specific parts of the model.

Visualization 52

Visualization Interactive Knowledge Discovery Modeling 52

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. anomaly detection).

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Ontotext

FEBRUARY 19, 2020

We hope it will bring some clarity to the topic and will help you get a better understanding of what it takes to craft a knowledge graph the semantic data modeling way. Ontotext’s 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling. Create your semantic data model. Make your KG easy to maintain.

Modeling 90

Modeling Knowledge Discovery Data Quality Technology 90

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. Again, the overall aim is to extract knowledge from data and, through algorithms based on artificial intelligence, to assist medical professionals in routine diagnostics processes.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

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. A consequence of the LSOS business model? Very low variable costs have two implications for the business model of these online services. They also tend to care about small effect fractions.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

APRIL 4, 2019

Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. Economy.bg: You work with media companies such as the BBC and the Financial Times. What exactly do you do for them?

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

The Unofficial Google Data Science Blog

JULY 22, 2020

In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a. These MAB algorithms are great at maximizing reward when the models are perfectly specified and probabilities are accurate.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Ontotext

AUGUST 10, 2023

Their interoperability and the supported network standards for communication enable devices to seamlessly connect and interact regardless of make, model, or operating system. They make this possible by adding domain knowledge that puts your organization’s data in context and enables its interpretation.

Technology 52

Technology Knowledge Discovery Interactive Enterprise 52

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

FineReport

JULY 16, 2021

You need the ability of data analysis to aid in enterprise modeling. It is a process of using knowledge discovery tools to mine previously unknown and potentially useful knowledge. It is an active method of automatic discovery. Data Analysis. OLAP is a data analysis tool based on data warehouse environment.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

Ontotext

APRIL 27, 2021

This is where experience counts and Ontotext has a proven methodology for semantic data modeling that normalizes both data schema and instances to concepts from major ontologies and vocabularies used by the industry sector.

Knowledge Discovery 59

Knowledge Discovery Unstructured Data Insurance Testing 59

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.

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

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 $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

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. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. 30(2–3), 195–215. link] Ling, C. X., & Li, C. Quinlan, J. Programs for machine learning.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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

Rare binary event example In the previous post , we discussed how rare binary events can be fundamental to the LSOS business model. Say we build a classifier to classify user sessions into two groups which we will call “dead” and “undead” to emphasize the importance of the rare purchase event to our business model.

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