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

IBM Big Data Hub

APRIL 15, 2024

Additionally, these accelerators are pre-integrated with various cloud AI services and recommend the best LLM (large language model) for their domain. IBM developed an AI-powered Knowledge Discovery system that use generative AI to unlock new insights and accelerate data-driven decisions with contextualized industrial data.

Manufacturing 92

Manufacturing Contextual Data Knowledge Discovery Data Lake 92

AWS Big Data

SEPTEMBER 26, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Data Lake 115

Data Lake Cost-Benefit Unstructured Data Modeling 115

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. In this post you will discover the aspects of the Semantic Web that are key to enterprise data, knowledge and content management. Source: tag.ontotext.com. What is it?

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

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. ExaMode, an acronym for Extreme-scale Analytics via Multimodal Ontology Discovery & Enhancement, is a project funded by the European Union, H2020 programme.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

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 Data mining Reporting 98

Ontotext

AUGUST 3, 2023

Buildings That Almost Think For Themselves About Their Occupants The first paper we are very excited to talk about is Knowledge Discovery Approach to Understand Occupant Experience in Cross-Domain Semantic Digital Twins by Alex Donkers, Bauke de Vries and Dujuan Yang.

Knowledge Discovery 52

Knowledge Discovery Broadcasting Technology Publishing 52

Ontotext

AUGUST 15, 2019

But it has enriched us in terms of identifying key needs for those looking to build a simple prototype in order to demonstrate the power of semantic technology, linked data and knowledge graphs. There, they can turn the acquired knowledge into a practical solution to their specific business case and strategize about its implementation.

Knowledge Discovery 70

Knowledge Discovery Technology Metadata Interactive 70

Ontotext

APRIL 6, 2023

Well, it’s all thanks to knowledge graphs. 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. This model is used in various industries to enable seamless data integration, unification, analysis and sharing.

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

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). Domain Knowledge. The foremost step of this process is to possess relevant domain knowledge regarding the problem at hand.

Data mining 77

Data mining KDD Data Science Forecasting 77

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. Hoewever, it can be a double-edged sword for enterprises. INTERFACE OF BI SYSTEM.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

The Unofficial Google Data Science Blog

JULY 22, 2020

However, if one changes assignment weights when there are time-based confounders, then ignoring this complexity can lead to biased inference in an OCE. In the case of MABs, ignoring this complexity can also lead to poor total reward, making it counterproductive towards its intended purpose.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

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. Knowledge graphs can be used to foster text analysis and make this easier, as in the Ontotext Platform. What Makes a Data Fabric? It is a buzzword.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

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. We present the inner workings of the SMOTE algorithm and show a simple “from scratch” implementation of SMOTE. Merging the two results in a completely balanced dataset (50:50).

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Ontotext

DECEMBER 1, 2023

Seen through the three days of Ontotext’s Knowledge Graph Forum (KGF) this year, complexity was not only empowering but key to the growth of knowledge and innovation. Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises. Cunningham.

Metadata 78

Metadata Sales Machine Learning Consulting 78

Ontotext

APRIL 27, 2021

The richness of data, if it can be discovered, enables the discovery of novel therapies, causal relationships or, just as important, retrieving existing negative results so that the company doesn’t spend millions of dollars to discover what is already known not to work. It is from those connections that new discoveries are made.

Knowledge Discovery 59

Knowledge Discovery Unstructured Data Insurance Testing 59

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 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

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

AUGUST 22, 2023

However, for this to happen, there needs to be context for the data to become knowledge. Worse, and according to Gartner, upward of 80% of enterprise data today is unstructured which further exacerbates the loss of knowledge, insights, and the wisdom needed to make effective business choices.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 52

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. For example, by using unique identifiers for each data item, knowledge graphs make it easy to identify resources across different datasets and systems.

Technology 52

Technology Knowledge Discovery Interactive Enterprise 52

Ontotext

AUGUST 16, 2019

But it has enriched us in terms of identifying key needs for those looking to build a simple prototype in order to demonstrate the power of semantic technology, linked data and knowledge graphs. There, they can turn the acquired knowledge into a practical solution to their specific business case and strategize about its implementation.

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

Ontotext

OCTOBER 28, 2021

In our previous post, we covered the basics of how the Ontotext and metaphacts joint solution based on GraphDB and metaphactory helps customers accelerate their knowledge graph journey and generate value from it in a matter of days. Today, users from the general public, journalists, etc.

Visualization 52

Visualization Interactive Knowledge Discovery Dashboards 52

Ontotext

APRIL 4, 2019

We apply Artificial Intelligence techniques to understand the value locked in this data so we can extract knowledge that can benefit people. Some of this knowledge is locked and the company cannot access it. into structured knowledge that can be processed by machines. On March 19, 2019, Economy.bg Machines Against Fake News.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

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 $f_theta$. The size and importance of these systems makes this hard.

KDD 40

KDD Testing Machine Learning Measurement 40

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.

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.

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

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. Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data. It is certainly true that for any given effect, statistical significance is an SMOD.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. Estimating confidence intervals with precision and at scale was one of the early wins for statisticians at Google. It has remained an important area of investment for us over the years. Both estimators are unbiased. But the latter estimator has less variance.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

CIO Business Intelligence

MARCH 26, 2025

The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data. How did we get here?

Knowledge Discovery 84

Knowledge Discovery Cost-Benefit Enterprise Modeling 84

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. Ontologies ensure a shared understanding of the data and its meanings.

Metadata 52

Metadata Knowledge Discovery Data Integration Management 52

Ontotext

AUGUST 21, 2024

There is a confluence of activity—including generative AI models, digital twins, and shared ledger capabilities—that are having a profound impact on helping enterprises meet their goal of becoming data driven. This is where building a Graph CoE becomes a critical asset because the journey to efficiency and enhanced capability must be guided.

Knowledge Discovery 52

Knowledge Discovery Cost-Benefit Data-driven Metadata 52