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The key to success is to start enhancing and augmenting content management systems (CMS) with additional features: semantic content and context. This is accomplished through tags, annotations, and metadata (TAM). TAM management, like content management, begins with business strategy. Collect, curate, and catalog (i.e.,
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We started with our marketing content and quickly expanded that to also integrate a set of workflows for data and content management. Our goal is to generate a knowledge space where information is easy to find, reuse, and fuel knowledge-driven insights. Where does AI fit into this?
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Poor data management, data silos, and a lack of a common understanding across systems and/or teams are the root cause that prohibits an organization from scaling the business in a dynamic environment. Enter the Semantic Edge Era: How to Derive Value from Semantic Metadata The problem with Big Data is not the data itself.
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Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
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Semantic technology is a broad technological term that covers specific technological approaches, principles and methodologies for managing data and knowledge. Let’s start with a quick definition of the basics. If we have to boil it down to the essentials – it deals with the meaning rather than the structure of the data.
However, Talk to Your Graph manages to answer us when we ask: We can modify the query template that ChatGPT uses for query generation to include additional filters such as author, creation time of the texts or others. Let’s see how we’ve approached this with our Ontotext Knowledge Graph project.
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