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If you include the title of this blog, you were just presented with 13 examples of heteronyms in the preceding paragraphs. This is accomplished through tags, annotations, and metadata (TAM). Smart content includes labeled (tagged, annotated) metadata (TAM). What you have just experienced is a plethora of heteronyms.
Metadata used to be a secret shared between system programmers and the data. Metadata described the data in terms of cardinality, data types such as strings vs integers, and primary or foreign key relationships. Inevitably, the information that could and needed to be expressed by metadata increased in complexity.
We store this in GraphDB by leveraging standard tooling for knowledge graph management. Through Ontotext Metadata Studio (OMDS), we then apply semantic content enrichment using text analysis based on our marketing vocabularies. In this way, we benefit from better SEO and semantic-driven content discovery.
Knowledge graphs (KG) came later, but quickly became a powerful driver for adoption of Semantic Web standards and all species of semantic technology implementing them. This way KGs help organizations smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment.
These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledgediscovery. Again, the overall aim is to extract knowledge from data and, through algorithms based on artificial intelligence, to assist medical professionals in routine diagnostics processes.
This means the creation of reusable data services, machine-readable semantic metadata and APIs that ensure the integration and orchestration of data across the organization and with third-party external data. Knowledge Graphs are the Warp and Weft of a Data Fabric. This provides a solid foundation for efficient data integration.
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. Some of that journey has been recorded in a previous blog post. What This Training Is.
Without metadata management and other data-related operations with semantic technologies, organizations often struggle to connect data sets and achieve a unified view of their enterprise data. Enter the Semantic Edge Era: How to Derive Value from Semantic Metadata The problem with Big Data is not the data itself.
Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.
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. Some of that journey has been recorded in a previous blog post. What This Training Is.
Graphs boost knowledgediscovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. million users. Linked Data, subscriptions, purchased datasets, etc.).
In this blog post, we dive into the capabilities of Ontotext’s semantic technology products and solutions that facilitate NLQ. Let’s see how we’ve approached this with our Ontotext Knowledge Graph project. Check out our NLQ with LangChain blog post for more ideas.
Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprise data management requirements across several verticals. As a hub for data, metadata, and content, they provide a unified, consistent, and unambiguous view of data scattered across different systems.
As a result, contextualized information and graph technologies are gaining in popularity among analysts and businesses due to their ability to positively affect knowledgediscovery and decision-making processes. The Graph CoE is an important step in transforming a lighthouse project or silo deployment into a true enterprise platform.
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