Have it All: Showcasing Semantic Graph and RAG at KMWorld

KMWorld is just around the corner, and after years of hosting separate booths as Squirro and Synaptica, we're heading back to Washington D.C. as one organization. On display will be the synergies unleashed by a technological match that traces right back to KMWorld – a powerful testament to the value of the trade show that brings together the biggest names in the knowledge management space. 

In July, Squirro announced the acquisition of Synaptica. The acquisition stems from challenges that many in Knowledge Management and Enterprise Search are likely familiar with, as generative AI continues to disrupt the entire field:

• How can we improve the accuracy of our GenAI-powered knowledge management platform?

• How can we scale our Enterprise AI deployment more effectively? 

For organizations with pre-existing enterprise taxonomies, another question arises: 

• How can we fully leverage the benefits of the enterprise taxonomies and ontologies we've developed? 

As we explore here, merging taxonomy and ontology management and enterprise GenAI contributes to addressing these challenges. Whether your project starts top-down – with an existing GenAI deployment, bottom-up – with a functional enterprise taxonomy and ontology, or from a clean slate, integrating these technologies creates a strong foundation for next-level GenAI capabilities. 

GraphRAG: Enhancing Accuracy, Reliability, and Trust of Enterprise AI

Once you've seen GenAI elevate the effectiveness of enterprise knowledge management tools, there's no way to unsee it. It's no surprise that a growing number of companies are embracing the enterprise AI solutions to optimize operations, automate content creation, personalize marketing, and drive data-driven decision-making. We continue to see amazing results – a 200% increase in efficiency, a 96% reduction in time, and a 40% reduction in incidents, just to name a few. 

However, the value of Enterprise GenAI hinges on the accuracy, reliability, and trustworthiness of its outputs. By supporting the large language models powering the enterprise GenAI applications with the most relevant corporate and third-party documents and data – identified using advanced search algorithms – retrieval augmented generation (RAG) dramatically improves the quality of the content, information, and insights generated. 

Semantic knowledge graphs take this a step further, elevating performance by providing LLMs with a deep contextual understanding of words, concepts, products, or processes and their relationships. Increasing the relevance, completeness, and precision of the data supplied to LLMs enables AI-based knowledge management systems to generate more accurate insights and more complete answers to prompts. 

Enhancing ROI from Existing Taxonomies and Ontologies

We've explored a typical top-down scenario, where an organization with an existing AI-based knowledge management system seeks to enhance its performance. Now, let's examine the opposite approach – from the perspective of an organization that has invested years developing an enterprise taxonomy to define key entities, along with an ontology that maps the relationships between them. 

Consider an organization that uses an enterprise taxonomy to improve search, guide navigation, and classify content. Centralizing their taxonomy in a taxonomy and ontology management system (TOMS) like Graphite establishes a single source of truth (SSOT) to standardize metadata across their platforms. This "un-silos" content by using consistent metadata across platforms, mitigates the risk of missing information with synonym-rich taxonomies that increase recall, and reduces time wasted on irrelevant results by classifying content by semantically disambiguated concepts (things) rather than ambiguous text words (strings).

It also lays the foundation for accurate enterprise GenAI. Through the integration of Squirro and Synaptica, the organization can develop an end-to-end workflow that builds on the pre-existing enterprise taxonomy, giving rise to a full-fledged GraphRAG platform.

• Taxonomies and ontologies provide the SSOT for content classification.

• The classifier automatically builds a content-aware Knowledge Graph (KG).

• The KG drives semantic search and also guides and controls the flow of conversational AI (GraphRAG). 

Connecting their RAG to a transparent and editable knowledge model, the customer benefits from less probabilistic – and more deterministic – AI conversations. This shift significantly boosts GenAI accuracy, enabling them to implement reliable business process automation. It's a welcome return on the long-term investment in creating and curating their enterprise taxonomy.

Combining Graph and RAG to Scale Effectively

Across industries, companies have kicked off initiatives to harness their enterprise data with GenAI-powered knowledge management. Unfortunately, many in-house GenAI PoCs never make it to production. After proving the technology's potential on hundreds of documents, organizations struggle to scale to millions, managing terabytes of data while ensuring accuracy, security, and compliance with data access rights.

To see how knowledge graphs can boost the accuracy of a GenAI system as it scales, consider this example: An organization indexes its enterprise data in a vector database containing hundreds of thousands of well-organized documents, each carefully chunked and tagged with relevant metadata.

Imagine asking the AI for a specific number found in a lengthy PDF. A standard vector search is likely to identify that the data is in the document, but it may need help to accurately provide the number. Perform the same search across multiple documents and the performance will typically decline even further.

Repeat the exercise on data classified against a well-crafted taxonomy and stored in a knowledge graph. In this case, the GenAI system can traverse the graph to locate the files containing the data and provide the exact number, giving the user full transparency into where the value was retrieved from in the graph.

Even at scale, knowledge graphs help maintain maximum accuracy while reducing the number of user interactions required to precisely pinpoint and retrieve the sought-after data.

Join us at KMWorld 2024

We look forward to learning about your projects at KMWorld 2024, where we'll showcase the Synaptica Graphite Taxonomy and Ontology Management Solution at booth #301 and our cutting-edge Enterprise GenAI Platform at booth #305.

We also look forward to discussing how these powerful technologies can transform your business, so be sure to stop by and learn how we can help you unlock the full potential of your enterprise data! Don’t want to wait until the event? Contact us today to start the conversation and explore how we can help you right now.