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This is accomplished through tags, annotations, and metadata (TAM). Smart content includes labeled (tagged, annotated) metadata (TAM). The key to success is to start enhancing and augmenting content management systems (CMS) with additional features: semantic content and context. Collect, curate, and catalog (i.e.,
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.
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”.
This becomes possible thanks to metadata enrichment, integrating and linking data from various data sources and, ultimately, dumping, structuring and querying that data with the help of a semantic graph database. One of the best success stories as a result of our training is Culture Creates.
This solution empowers organizations to unlock their data assets’ potential across industries like research, legal, healthcare, enterprise knowledge management, customer experience, and marketing. Explore the AWS Serverless Data Analytics Pipeline reference architecture and take advantage of the power of Amazon Bedrock.
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.).
This becomes possible thanks to metadata enrichment, integrating and linking data from various data sources and, ultimately, dumping, structuring and querying that data with the help of a semantic graph database. One of the best success stories as a result of our training is Culture Creates.
Let’s see how we’ve approached this with our Ontotext Knowledge Graph project. How Ontotext uses RAG We build on top of our products GraphDB and Ontotext Metadata Studio to develop a content enrichment system. We provide visibility of where the source information for each of the stated facts in the generated answer came from.
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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