Integrating Unstructured Data Sources with Knowledge Graph

Integrating Unstructured Data Sources into a Knowledge Graph


Several of our customers are leveraging Anzo to build scalable, complex knowledge graphs that stretch across diverse sets of structured and unstructured data. Let's take a look at a mission-critical search application backed by a knowledge graph to better understand Anzo's support of unstructured data.

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The Production Use Case

A large US company needs to build an integrated, large-scale search application that leverages data from multiple applications across the enterprise.

Unstructured and structured data from these previously siloed applications must be harmonized into a single, flexible model on which the search application is built.

Anzo delivers the scalable, comprehensive knowledge graph that brings these numerous structured and unstructured data sources together.

There are a number of complex technical requirements:

1 Allow analysts to execute text-based searches across millions of documents in real-time.
2 Accommodate hundreds of thousands of new documents added to the knowledge base daily.
3 Enable complex filtering and sorting of text-based searches with additional criteria of related but distinct metadata attributes.
4 Support the development and use of a flexible data model (Ontology) that harmonizes structured and unstructured data.
5 Incorporate and surface results from a cloud-based, ML-driven text analytics engine used for document classification; facilitate development, testing and validation of this analytics engine to further improve its efficacy over time.

This case study showcases one example of a production customer using Cambridge Semantics’ knowledge graph platform, Anzo, to fulfill all these requirements.

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