Fundamentals
Retrieval Augmented Generation enhances LLMs with external knowledge for more accurate, contextual question answering.
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Taxonomy Management helps enterprises organize their knowledge and make information easy to find, use and analyze. Learn more!
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The benefits of the semantic layer in organizing and abstracting enterprise data to facilitate decision-making.
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Knowledge graphs are a collection of interlinked descriptions of entities that put data into context and enable data analytics & sharing.
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Large Language Models (LLMs) are Machine Learning models that are trained on massive datasets of text to understand, summarize, generate and predict l
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Metadata represents data about data. It enriches data with information which makes it easier to discover, use, and manage.
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An ontology is a rulebook defining concepts within a specific domain, their properties, and their relationships, ensuring a common understanding of in
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Auto Classification helps enterprises organize their knowledge and make information easy to find, use, and analyze. Learn more!
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Semantic text intelligence focuses on using computational methods to “understand” unstructured text, extract meaningful facts, and integrate them into
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Semantic search uses knowledge graphs & text analysis to understand the intent and context behind a query, rather than just matching keywords.
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Entity linking is the process of identifying mentions of entities in text and connecting them to their unique identity in a knowledge base.
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NLQ empowers users to interact and “talk” to their data by using LLMs to bridge the gap between human questions and complex databases.
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The Semantic Web and Semantic Technology aim at a machine-readable internet where data is interconnected and meaningful.
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Data Fabric is a modern approach to data management that addresses the challenges of increasingly complex and distributed data landscapes through the
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Data mesh is a decentralized, domain-centric operational framework and architecture for managing data for establish better data ownership.
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SPARQL is the standard query language and protocol for Linked Open Data on the web and RDF databases, designed by the W3C.
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SHACL is a W3C standard for describing conditions that a dataset must meet and a powerful tool for improving knowledge graph quality.
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RDF is a standard for data interchange that is used for representing highly interconnected data.
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GraphQL is a powerful and flexible query language for APIs, offering a robust alternative to traditional REST approaches.
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Explore how to start your journey of building knowledge graph in these 10 steps.
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PoolParty helps companies build a business taxonomy with ease. Learn how to get started with a taxonomy and design best practices.
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