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Motivated by our marketing team’s aim to simplify content discovery on our website, we initiated the Ontotext Knowledge Graph (OTKG) project. We started with our marketing content and quickly expanded that to also integrate a set of workflows for data and content management. What is OTKG?
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 Datadiscovery tools available in the market to take their brand forward.
An inaccurate AI prediction in a marketing campaign is a minor nuisance, but an inaccurate AI prediction on a manufacturing shopfloor can be fatal. For example, as manufacturers, we create a knowledge base, but no one can find anything without spending hours searching and browsing through the contents.
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 DataDiscovery tools available in the market to take their brand forward.
Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset.
It enriched their understanding of the full spectrum of knowledge graph business applications and the technology partner ecosystem needed to turn data into a competitive advantage. Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises.
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).
Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Several factors are driving the adoption of knowledge graphs. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions.
While there are many other varying definitions that exist, our definition of the knowledge graph places emphasis on defining the semantic relations between entities, which is central to providing humans and machines with context and means for automated reasoning.
This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. Natural Language Query (NLQ) has gained immense popularity due to its ability to empower non-technical individuals to extract data insights just by asking questions in plain language.
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