Google popularized the term "knowledge graph" in this 2012 blog post. Since then, there has been a massive momentum around ...

Data Quality and Consistency: Ensuring accuracy and consistency in the data is crucial for the reliability of a knowledge graph. Scalability: As data volume grows, the knowledge graph must ...

Knowledge graphs: The link between data and meaning While Google popularized the term “knowledge graph” in 2012, the concept of representing knowledge as interconnected information has roots ...

It involves understanding metadata knowledge graphs and how different layers of the modern data stack come together. If one wants to do anything with data, they need a stack of tools to get it done.

The journey from unstructured data (texts, images, etc.) to a fully structured knowledge graph—rich in facts, logical constraints, and recursive rules—is complex and challenging, but the ...

Knowledge graphs allow users to query complex relationships between pieces of data (Image courtesy Ontotext) Knowledge graph databases are like multi-dimensional maps of the example mentioned earlier.

An essential component for building more trustworthy AI lies in creating a more solid data foundation on top of graph databases, knowledge graphs, and vector databases. Each element plays a ...

The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...