How Pharma Companies Can Scale Up Their Knowledge Discovery with Semantic Similarity Search 

Ontotext

Pharma has deep roots in human history with centuries of folk pharmaceutical knowledge offering a hit-and-miss range of natural remedies. From this processed data a knowledge graph (KG) is created.

Knowledge Discovery, Data Gravity, and AI: Usama Fayyad & Gregory Piatetsky-Shapiro on the Data and AI Legends Podcast

KDnuggets

Public understanding of AI applications usually goes through a phase shift, from "it cannot be done" to "of course, a computer can do it". KDnuggets Originals Data Science

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Data Mining Use Cases

TDAN BI

Big Data Blogs Big Data News, Articles, & Education Business Intelligence Blogs Business Intelligence News, Articles, & Education Data Blogs Data Education Data Strategy Blogs Data Strategy News, Articles, & Education Data Topics data mining knowledge discovery lau use cases

AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

We apply Artificial Intelligence techniques to understand the value locked in this data so we can extract knowledge that can benefit people. Milena Yankova : Our work is focused on helping companies make sense of their own knowledge. Some of this knowledge is locked and the company cannot access it. into structured knowledge that can be processed by machines. ” Then this knowledge can be downloaded from the network. Source: Economy.bg.

KDD 2020 Opens Call for Papers

Data Science 101

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Topics of interest include artificial intelligence, big data, data analytics, data science, data mining, deep learning, knowledge graphs, machine learning, relational databases and statistical methods.

KDD 72

Human Participation - Still an indispensable element in Business Analytics

DataFloq

Business Analytics synergizes the strengths of various sciences including data mining, knowledge discovery, machine learning, pattern recognition, statistics, neurocomputing, and artificial intelligence. Business Analytics has evolved a lot. Business Analytics was designed for addressing the need for deriving intelligence out of ‘data’, which is nowadays referred to affectionately by many as the ‘crude oil’ or ‘gold ore’ of modern times.

KDD 2020 Call for Research, Applied Data Science Papers

KDnuggets

ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, Knowledge Discovery and Machine Learning for 26 th Annual Conference in San Diego. 2019 Dec Events Applications CA KDD KDD-2020 Research San Diego

KDD 40

Education Trends 2022: Data Science in schools

DataFloq

Combining large amounts of information with existing tools is possible using the formalized knowledge discovery models in Data Science or Data Mining techniques. Data Science is a growing field that has emerged in many key areas of our world.

Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

Labeling, indexing, ease of discovery, and ease of access are essential if end-users are to find and benefit from the collection. Contextual TAM enhances a CMS with knowledge-driven search and retrieval, not just keyword-driven. Labels are curated and stored with the content, thus enabling curation, cataloguing (indexing), search, delivery, orchestration, and use of content and data in AI applications, including knowledge-driven decision-making and autonomous operations.

On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. And this is what Ontotext’s role in the project is about: knowledge graphs. Blog Business ExaMode knowledge graph supercomputer

Understanding Social And Collaborative Business Intelligence

datapine

Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward. 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. It also facilitates BI tool user adoption, allowing the organization to share knowledge and resources.

Knowledge Graphs and Healthcare

Ontotext

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.

At Center Stage: 2 Ontotext Webinars About Reasoning with Big Knowledge Graphs and the Power of Cognitive Graph Analytics

Ontotext

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. Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes.

Unlocking the Power of Better Data Science Workflows

Smart Data Collective

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.

Business Intelligence System: Definition, Application & Practice

FineReport

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. In daily work, when business develops to a relatively large scale, we will all face variable management problems.

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. What can it do and how are enterprise knowledge graphs related to it?

Crafting a Knowledge Graph: The Semantic Data Modeling Way

Ontotext

The term “knowledge graph” (KG) has been gaining popularity for quite a while now. 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.

How Do Super Rookies Start Learning Data Analysis?

FineReport

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.

Designing a SemTech Proof-of-Concept: Get Ready for Our Next Live Online Training

Ontotext

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. Semantic technology is a broad technological term that covers specific technological approaches, principles and methodologies for managing data and knowledge. This will lay solid foundations for the rest of the work necessary for implementing a full, working knowledge graph solution.

At Center Stage VI: Ontotext and Semantic Web Company on Creating and Scaling Big Enterprise Knowledge Graphs

Ontotext

But no system, even such a highly mature RDF database for knowledge graphs as ours, is an island. The very first post of this series presented two knowledge graph maps with 20+ applications and 30+ capabilities. Knowledge Graphs: 5 Use Cases and 10 Steps to Get There.

Ontotext’s Most Popular Blog Posts for 2019

Ontotext

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. Here’s our countdown: It Takes Two to Tango: Knowledge Graphs and Text Analysis.

Three’s Company Too: Metadata, Data and Text Analysis

Ontotext

To ensure the metadata extracted is of high-quality, Ontotext uses knowledge graphs to increase the performance of its text analysis services. The text analysis is also able to identify gaps in the knowledge graph such as concepts and relationships previously unidentified.

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Conference on Knowledge Discovery and Data Mining, pp. 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.

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed.

From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

Knowledge Graphs are the Warp and Weft of a Data Fabric. Given a critical mass of domain knowledge and good level of connectivity, KG can serve as context that helps computers comprehend and manipulate data. Ontotext’s Platform for Enterprise Knowledge Graphs.

Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Fundamentals of Data Mining

Data Science 101

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. To anyone looking at a large pile of data, it may seem like a collection of junk unless the person has the background knowledge and information about the business.

Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

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.

Designing a SemTech Proof-of-Concept: Get Ready for Our Next Live Online Training

Ontotext

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. Semantic technology is a broad technological term that covers specific technological approaches, principles and methodologies for managing data and knowledge. This will lay solid foundations for the rest of the work necessary for implementing a full, working knowledge graph solution.

GraphDB and metaphactory Part II: An RDF Database and A Knowledge Graph Platform in Action

Ontotext

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. Building a Knowledge Graph Application with metaphactory.

Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

This knowledge has influenced our decision-making way beyond the concrete cases we studied in detail. We use this knowledge to define objective functions to optimize our ads system with a view towards the long-term. 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]

Accelerating model velocity through Snowflake Java UDF integration

Domino Data Lab

The data science team grows and people can’t work in isolation anymore – they need to be able to share knowledge, handover projects, and have validation and reproducibility capabilities at their disposal.

Understanding Social And Collaborative Business Intelligence

datapine

Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data Discovery tools available in the market to take their brand forward. 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. It also facilitates BI tool user adoption, allowing the organization to share knowledge and resources.

Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

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, DCby AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science.

Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013. [3] by OMKAR MURALIDHARAN Many machine learning applications have some kind of regression at their core, so understanding large-scale regression systems is important. But doing this can be hard, for reasons not typically encountered in problems with smaller or less critical regression systems.

LSOS experiments: how I learned to stop worrying and love the variability

The Unofficial Google Data Science Blog

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, DCby AMIR NAJMI In the previous post we looked at how large scale online services (LSOS) must contend with the high coefficient of variation (CV) of the observations of particular interest to them.