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Ontotext Marketing Gets a Boost from Knowledge Graph Powered LLMs

Ontotext

We envisioned harnessing the power of our products to elevate our entire content publishing process, thereby facilitating in-depth knowledge exploration. We started with our marketing content and quickly expanded that to also integrate a set of workflows for data and content management.

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Understanding Social And Collaborative Business Intelligence

datapine

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. 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.

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

Ontotext

It demonstrates how GraphDB and metaphactory work together and how you can employ the platform’s intuitive and out-of-the-box search, visualization and authoring components to empower end users to consume data from your knowledge graph. Data normalization is an essential step in the data preparation process.

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Understanding Social And Collaborative Business Intelligence

datapine

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. 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.

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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. And what are the commercial implications of semantic technologies for enterprise data? Source: tag.ontotext.com.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. 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. 1998) and others).