Artificial intelligence (AI) has captured the interest of data scientists, tech leaders, and anyone using data for business decisions (and, of course, the general public). Enterprises want to enhance insights and productivity with their AI stacks. Poor quality datasets are the main obstacle for organizations. Data sources need to be well-defined and clean to leverage your AI stack, whether that be with large language models (LLMs) or other machine learning techniques.

The value of an AI project relies on the breadth, depth, and quality of its datasets. Quality datasets require a solid foundational technical stack because data integration is a crucial layer when developing AI models. This step prepares clean, accurate data. Data integration is an essential step for building reliable, effective data solutions. 

What Makes Knowledge Graphs Valuable?

Analysts have a lot to say about technologies that play an eminent role in data integration. Knowledge graphs offer a comprehensive, organizational context for data analytics solutions, especially those to employ your AI stack. Knowledge graphs were once considered niche technology. However, knowledge graphs are increasingly considered critical enablers of data integration and model-building in the AI revolution. Gartner notes that: “The range for knowledge graphs is now, as [knowledge graph] adoption has rapidly accelerated in conjunction with the growing use of AI, generally, and large language models, specifically. GenAI models are being used in conjunction with [knowledge graphs] to deliver trusted and verified facts to their outputs, as well as provide rules to contain the model.”

In an ideal world, data analysts choose well-described data points from a “single pane of glass.” Siloed data sources integrate, aggregate, and harmonize data into a set of parameters to feed custom algorithms. A quote from McKinsey explores this further: “Context can be determined only from existing data and information across structured and unstructured sources. To improve output, CDOs will need to manage integration of knowledge graphs or data models and ontologies (a set of concepts in a domain that shows their properties and the relations between them) into the prompt.”

This quote highlights two key advantages of knowledge graphs:

1. Knowledge graphs connect unstructured context, like files and PDFs, to structured data (unlike relational databases).
   
2. Semantic layers natively express the relationships between data concepts. Ontologies in knowledge graph provide a semantic layer, allowing unstructured content to connect to structured data.

Altair’s Knowledge Graph Offering

Altair® Graph Studio™ is a comprehensive toolset within the Altair® RapidMiner® data analytics and AI platform. Graph Studio’s architecture enables users to construct knowledge graphs dynamically. Graphmarts are unique structures that create knowledge graphs by overlaying and combing unstructured or structured data from diverse sources. Graphmarts support valuable functionalities and are optimal frameworks for efficiently creating knowledge graphs:

• In-memory activation: With the resource description framework (RDF) knowledge graph engine, each data source becomes an activated in-memory layer. Knowledge graphs allow users to seamlessly add additional layers. This creates logical connections, extensions, and transformations. Data movement is limited between sources and the knowledge graph.
• Codeless workflows: Users can connect, map, and cleanse data effortlessly without the need for coding.
• Massively parallel processing (MPP) query engine: Users can load data with no inspection and use knowledge graphs to cleanse it. Additionally, with the computational intensity of AI tasks, the MPP query engine runs queries to save resources for downstream applications. 

As shown above, Graph Studio represents data using ontologies. Graph Studio provides several advantages over relationships.

• Structured knowledge representation: Ontologies represent knowledge in a structured way. They define concepts, relationships, and categories within a domain. This helps organize, disambiguate, and contextualize data. A model’s understanding of relationships and hierarchies in data is enhanced when structured data is integrated into LLMs. The result is more accurate and contextually relevant responses.
• Domain-specific customization: A knowledge base for LLMs is provided through ontologies that are tailored to specific domains. This is beneficial in fields such as healthcare, manufacturing, law, or engineering, where domain-specific knowledge is crucial for creating accurate and reliable content.
• Enhanced learning and adaptability: Together, ontologies and generative AI (genAI) models facilitate continuous learning. AI models adapt and refine their outputs, leading to a system that improves over time. 
• Scalability and efficiency: Ontologies open the door for easier data management and querying. Relationships are more efficiently represented in ontologies than relational databases. This translates into faster, more scalable responses from genAI models, especially when there are large volumes of data or complex information networks.

Using knowledge graphs, tech leaders seamlessly integrate new and existing datasets to improve their operations. To learn more about knowledge graphs, contact us.