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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Linked Data, subscriptions, purchased datasets, etc.).

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From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

Consider using data catalogs for this purpose. Clean data to ensure data quality. Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc.

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KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

Ontotext

In an engaging narrative built on the premise that most organizations are not ready for a knowledge graph, Lance talked about the usual pitfalls when building such a solution. According to him, “failing to ensure data quality in capturing and structuring knowledge, turns any knowledge graph into a piece of abstract art”.

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Crafting a Knowledge Graph: The Semantic Data Modeling Way

Ontotext

Clean your data to ensure data quality. Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc. Maximize the usability of your data.

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

Ontotext

We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. Ontotext was founded in 2000 with the Semantic Web in its genes and we had the chance to be part of the community of its pioneers. We can’t imagine looking at the Semantic Web as an artifact.

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Accelerating model velocity through Snowflake Java UDF integration

Domino Data Lab

Advanced data wrangling and preprocessing pipelines A Java UDF can use a wider range of techniques for data cleansing, feature engineering, and mode advanced preprocessing, compared to what is available in SQL. Existing preprocessing, data ingestion, and data quality processes can be converted from Java/Spark into Java UDFs.

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The Importance of the Semantic Knowledge Graph

Ontotext

As this previous blog post discusses, it additionally simplifies governance for the individual assets since vocabularies can be maintained outside of the ontology and help ensure data quality. Unlock the full potential of your data!