IBM Big Data Hub

APRIL 15, 2024

Various initiatives to create a knowledge graph of these systems have been only partially successful due to the depth of legacy knowledge, incomplete documentation and technical debt incurred over decades. Summarization Summarization remains the top use case for generative AI (gen AI) technology.

Manufacturing 103

Manufacturing Contextual Data Knowledge Discovery Data Lake 103

Rocket-Powered Data Science

JULY 6, 2021

Techniques that both enable (contribute to) and benefit from smart content are content discovery, machine learning, knowledge graphs, semantic linked data, semantic data integration, knowledge discovery, and knowledge management. Decide and act on the delivered insights and knowledge. Can you find them all?

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

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

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

datapine

NOVEMBER 19, 2019

One of the most imperative features of social BI is its ability to create self-served and user-generated analysis, coupled with the application of business user knowledge. This can be done with the help of socializing ideas within an Enterprise Business Intelligence tool, be it with or without an Enterprise Social Network (ESN).

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Unstructured Data 146

Smart Data Collective

MARCH 13, 2020

Phase 4: Knowledge Discovery. My aim with any notebook is to enable someone to pick it up without any prior knowledge of the project and fully understand the analysis, decisions made and what the final output means,” Osborne explains. “My 5 Tips for Better Data Science Workflows. Use the Proper Mix of Hardware and Software.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

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. How do they know all this? This can lead to operational cost cutting and improve competitiveness.

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

Ontotext

AUGUST 26, 2021

This post continues the series of posts we started with At Center Stage: 2 Ontotext Webinars About Reasoning with Big Knowledge Graphs and Power of Graph Analytics. The first Ontotext webinar that takes center stage in this post is Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes. Why is that?

Knowledge Discovery 52

Knowledge Discovery Analytics Enterprise Optimization 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

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

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. “Complexity is empowering”, argues Howard G. Cunningham. The question is not how to avoid complexity but how to embrace it and take advantage of it.”

Metadata 78

Metadata Sales Consulting Enterprise 78

Ontotext

FEBRUARY 19, 2020

Although there is still no single, universally accepted definition, there have been various attempts at it – such as in Towards a Definition of Knowledge Graphs. Yet, the concept of knowledge graphs still lives without an agreed-upon description or shared understanding. There is diversity in terms of its implementation as well.

Modeling 90

Modeling Knowledge Discovery Data Quality Visualization 90

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

FineReport

JULY 16, 2021

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. With the advancement of information construction, enterprises have accumulated massive data base. Hoewever, it can be a double-edged sword for enterprises.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

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. Linked Data, subscriptions, purchased datasets, etc.).

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 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

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 19, 2020

To ensure the metadata extracted is of high-quality, Ontotext uses knowledge graphs to increase the performance of its text analysis services. The knowledge graph holds the ontology, the description of the concepts and their relationships, and the existing instances of those concepts and relationships. Semantics Makes It Powerful.

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. What is a knowledge graph?

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

Ontotext

JULY 29, 2021

In this post you will discover the aspects of the Semantic Web that are key to enterprise data, knowledge and content management. What can it do and how are enterprise knowledge graphs related to it? While Semantic Web is often condemned for being too academic, two of its incarnations already enjoy massive adoption. What is it?

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Ontotext

SEPTEMBER 15, 2020

Knowledge Graphs are the Warp and Weft of a Data Fabric. Knowledge graphs can be used to foster text analysis and make this easier, as in the Ontotext Platform. In other words, knowledge graphs solve the data silo problem by making it irrelevant. What Makes a Data Fabric? It is a buzzword.

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

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

AUGUST 1, 2021

This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. It is not possible to fully understand the inferential process of a deep neural network and to prove that it would generalise as expected. According to Fox et al.,

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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

Domino Data Lab

DECEMBER 4, 2020

When analysing pharmacokinetic data to determine the degree of exposure of a drug and associated pharmacokinetic parameters (e.g., clearance, elimination half-life, maximum observed concentration ( ), time where the maximum concentration was observed ( ), Non-Compartmental Analysis (NCA) is usually the preferred approach [1]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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. Semantic Data Integration With GraphDB.

Visualization 52

Visualization Interactive Knowledge Discovery Dashboards 52

Domino Data Lab

MAY 20, 2021

Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. If, however, the dataset is imbalanced with a class ratio of 100:1, this means that it contains only 100 examples of the minority class. Generation of artificial examples.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

For example in ads, experiments using cookies (users) as experimental units are not suited to capture the impact of a treatment on advertisers or publishers nor their reaction to it. Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1].

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

JUNE 15, 2021

Generally speaking, a User Defined Function (UDF), in the context of relational databases, is a mechanism for extending the functionality of the underlying database. Moreover, such a function can be evaluated within standard SQL queries, but it can be written in a language different to SQL. Most importantly, UDFs are executed directly in Snowflake.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

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 95

Experimentation Statistics Testing Strategy 95

datapine

NOVEMBER 19, 2019

One of the most imperative features of social BI is its ability to create self-served and user-generated analysis, coupled with the application of business user knowledge. This can be done with the help of socializing ideas within an Enterprise Business Intelligence tool, be it with or without an Enterprise Social Network (ESN).

Business Intelligence 20

Business Intelligence Knowledge Discovery Unstructured Data Software 20

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

JANUARY 14, 2016

Indeed, understanding and facilitating user choices through improvements in the service offering is much of what LSOS data science teams do. As with any enterprise, the goal of the service provider is to better satisfy its users and further its business objectives. It is certainly true that for any given effect, statistical significance is an SMOD.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

In the examples above, we might use our estimates to choose ads, decide whether to show a user images, or figure out which videos to recommend. These decisions are often business-critical, so it is essential for data scientists to understand and improve the regressions that inform them. The size and importance of these systems makes this hard.

KDD 40

KDD Testing Machine Learning Modeling 40

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 40

Experimentation Metrics Statistics Measurement 40