Blog: Why New AI Tools Like ChatGPT Need Knowledge Graphs
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Data Cataloging with Knowledge Graphs

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Today, data catalogs are increasingly recognized as a central mechanism for data management. They have become a critical building block for helping organizations find, inventory, connect and analyze their diverse and distributed data assets — in order to optimize their business use and value. Knowledge graphs are a key technology for data cataloging because they can meaningfully capture and connect the vast variety of enterprise data sources. They can eliminate data and metadata silos, delivering high-value business applications such as complete end-to-end data lineage and “Google-like” semantic search over metadata. In this webinar we will discuss:

  • Why flexibility, extensibility and open APIs are a must for data catalogs
  • What enterprise data catalogs (internal to an organization) and open data catalogs in government (e.g., using the standards-based DCAT vocabulary) share in common
  • The role of knowledge graphs in capturing information about diverse and siloed data assets and in creating semantic relationships between them
  • How assets in a data catalog can be contextualized by connecting them to relevant processes, policies and other business information
  • How inferencing and machine learning can automate and simplify the process of tagging and connecting data assets
  • Why data catalogs are an important step in supporting enterprises’ move towards data lakes

Today, data catalogs are increasingly recognized as a central mechanism for data management. They have become a critical building block for helping organizations find, inventory, connect and analyze their diverse and distributed data assets — in order to optimize their business use and value. Knowledge graphs are a key technology for data cataloging because they can meaningfully capture and connect the vast variety of enterprise data sources. They can eliminate data and metadata silos, delivering high-value business applications such as complete end-to-end data lineage and “Google-like” semantic search over metadata. In this webinar we will discuss:

  • Why flexibility, extensibility and open APIs are a must for data catalogs
  • What enterprise data catalogs (internal to an organization) and open data catalogs in government (e.g., using the standards-based DCAT vocabulary) share in common
  • The role of knowledge graphs in capturing information about diverse and siloed data assets and in creating semantic relationships between them
  • How assets in a data catalog can be contextualized by connecting them to relevant processes, policies and other business information
  • How inferencing and machine learning can automate and simplify the process of tagging and connecting data assets
  • Why data catalogs are an important step in supporting enterprises’ move towards data lakes
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