Supercharge your AI with Graphs

A graph is an incredibly powerful tool for connecting knowledge. It does this by explicitly showing the relationships between different pieces of information, much like how humans link existing knowledge with new data. By creating "nodes" for individual data points and "edges" to define their connections, a graph provides a visual and structured representation. This allows organizations to combine internal and public data, mirroring how individuals integrate new information into their current understanding. Continuously refining these connections enables a graph to adapt and effectively leverage both private and public information, tailored to specific organizational contexts and boosting productivity.

Consider a common challenge in pharmaceutical research: comparing interactions of molecules with each other and with biological entities. Existing information on these interactions is found in various public and private data sources.

Public: Academic publications, public databases, and standards on taxonomy and ontology.

Private: Databases containing proprietary information.

Pharma research organizations leverage both public and private information to query existing data on compounds and biological entities, and to compare compound properties and interactions using machine learning.

This required information is scattered across public and private sources, much like pieces of a puzzle. We utilize knowledge graphs as a structured database to integrate this disparate information, fitting the puzzle pieces together and significantly enhancing research productivity.

One might wonder, "Don't Large Language Models (LLMs) already implicitly link similar concepts? What do we need graphs for?"

1. Explicit relationships and reduced hallucinations: LLMs implicitly capture relationships through a general idea of "similarity." Knowledge graphs, however, make these relationships explicit using prior knowledge of ontologies. This significantly reduces "hallucinations," a common error in LLMs.

2. Contextual enhancement and usability: LLMs are trained on vast amounts of publicly available data. Knowledge Graphs enhance this by mapping relationships between concepts based on an organization's specific context and value creation. This helps tailor information to the organization's context, thereby improving its usability.

Think of LLMs and Knowledge Graphs like the broad, language-based communication we have as individuals, and the underlying representation and connections between concepts that we all seemingly leverage daily. Each of us maintains a unique Knowledge Graph in our minds, that’s evolving based on private and publicly available information.

Similarly, an organization's Knowledge Graph is unique, which has the potential for significant competitive advantage. It integrates diverse data to accelerate research and enhance decision-making in various industry-specific contexts.

Look out for our future post, which will delve deeper into Knowledge Graphs, how they enhance LLMs, and their role in business value creation, along with a case study!

Vinodh Venkatesan