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Leveraging knowledge graphs to drive GenAI success

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The potential for generative AI (GenAI) in a knowledge context is expansive, though so too are its challenges and roadblocks. Between hallucinations, consistency, security, and accuracy, grounding GenAI in a robust data environment is critical toward its success.

Sean Martin, CTO and co-founder of Cambridge Semantics, joined KMWorld’s webinar, How Knowledge Graphs Make Generative AI Consumable in Enterprise Environments, to discuss how knowledge graph environments are the key toward driving GenAI success in a proprietary context.

According to Martin, GenAI and knowledge graph technology is extremely synergistic with one another, explaining that “knowledge graphs have become an essential piece of infrastructure for those building more advanced generative AI solutions.”

Why are knowledge graphs so crucial for GenAI implementation?

For these AI applications, including the popular ChatGPT, its answers—as the name would suggest—are generated, not retrieved. Meaning, GenAI apps can be good for low stakes tasks, but if required to be accurate and relevant most of the time, its learned patterns will inevitably fail.

“The generative AI strategy is good—and getting better—at generating output that looks generally similar to examples in its training data, but it is not good at generating output that satisfies specific criteria, and the more criteria it has to satisfy, the worse it will do,” he added.

Knowledge graphs ground AI and large language models (LLMs) in context to avoid inaccuracies or hallucinations, increasing the precision of its output. Knowledge graphs:

  • Simplify access to complex data, both structured and unstructured, to address unanticipated questions
  • Quickly profile, connect, and harmonize data from multiple sources
  • Present tailored views, services, and experiences to different personas with conceptual models
  • Flexibly accommodate new data sources and use cases on the fly, with minimal impact

Leveraging knowledge graphs to support GenAI solutions can result in a myriad of benefits, ranging from higher quality outputs to ontology-driven alignment, scalability and semantic precision, and more, according to Martin.

With Cambridge Semantics’ Knowledge Guru—a conversational BI prototype that integrates OpenAI's ChatGPT with CSI's Anzo Knowledge Graph Platform—enterprises can empower natural language data access that doesn’t sacrifice quality, privacy, or precision. As a highly scalable technology, Knowledge Guru seamlessly integrates with existing data infrastructures, democratizing data access and propelling truly data-driven decision-making.

For the full, in-depth discussion of knowledge graph and GenAI implementation, you can view an archived version of the webinar here.

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