The potential of AI’s Large Language Models (LLMs) is vast, but I firmly believe that their true power for business enterprises multiplies when combined with Knowledge Graph technology. By integrating LLMs with Knowledge Graphs, enterprises can create AI applications that are not only smarter and more accurate but also more trustworthy and aligned with corporate goals. In this article, I’ll share four ways that Knowledge Graph tech can enhance LLM AI apps, and vice versa, to drive business success.

1. Knowledge Graph Tech Links Enterprise Data Together to Make LLM’s More Contextually Aware.

First, Knowledge Graphs enable LLMs to become more contextually aware by linking disparate data sources together. This is exactly what most businesses need – a seamless way to connect enterprise data silos.  Without a doubt, LLMs can rapidly leverage a vast amount of digital knowledge. However, they also can “hallucinate” when dynamically pulling together seemingly disparate information. This is not the case with Knowledge Graphs – they excel at answering multi-hop questions across a wide range of data sets. Below is an example of how knowledge graphs and LLMs can work together to make AI apps more contextually aware. Also, I’ll list the advantages of using knowledge graphs to help LLMs link data together.

a. Example of Knowledge Graphs Helping LLMs to Link Data Together: Medical Diagnoses.

As discussed, knowledge graphs serve as a structured and comprehensive foundation of real-world facts and relationships that can be used to bolster Large Language Models (LLMs) with common sense. For example, let’s look at a Knowledge Graph supporting a LLM AI app for medical diagnoses. First, AI developers can use a knowledge graph that contains an interconnected web of symptoms, diseases, medications, and patient histories. As a result, this allows the AI to understand the complex relationships and nuances within medical data. More importantly, the AI can provide more accurate and contextually informed diagnoses. Hence, this reduces both the likelihood of AI overlooking critical information and, worse hallucinating, making an incoherent diagnosis.

b. Advantages of Knowledge Graphs Helping LLMs to Link Data Together.

Below are the advantages of LLMs leveraging knowledge graphs to provide contextual-aware answers.

• Provides AI Broader Context. AI apps are able to follow relationships within enterprise knowledge graphs to gain insights not possible using just LLMs alone.
• Enables Task-Aware Relevance. With Knowledge Graphs, AI apps have a factual reference to filter and rank information to formulate better responses to user queries.
• Linkage to Full Range of Data Types. A Knowledge Graph supports the integration of both structured and unstructured data sources.

For more on knowledge graphs helping LLL to Link disparate data, see Tomaž Bratanič’s article, How to Improve Multi-Hop Reasoning With Knowledge Graphs and LLMs

“Multi-hop question answering is a groundbreaking approach that harnesses the joint power of Knowledge Graphs and LLMs to contextualize elaborate questions and provide accurate answers.”

IngestAI

2. Keep LLMs Up-To-Date With the Latest Information Using Knowledge Graphs.

One of the most significant and expensive challenges with LLMs is keeping them up-to-date with the latest information. Without a doubt, this is where Knowledge Graphs can help address this issue by providing a dynamic and constantly updated knowledge base. By integrating Knowledge Graphs with LLMs, developers can ensure that their AI applications remain current and informed, even in rapidly changing domains. See below for examples and advantages of using Knowledge Graphs to enable LLMs access to up-to-date information.

a. Example of Knowledge Graphs Keeping LLMs Up-To-Date: Financial Market Analysis

When knowledge graphs are continuously updated, they can significantly enhance LLMs. Specifically, knowledge graphs can provide AI apps the latest information to enable LLMs to better process user query prompts and to generate responses based on the latest information. For instance, in the context of financial market analysis, a real-time knowledge graph can include the latest stock prices, market trends, and news events. As a result, LLMs are able to deliver more timely and relevant investment insights.

To illustrate, let’s consider an investor who asks an AI app for help to understand what a merger between two tech firms could do. If the AI has current info from a knowledge graph, it can provide better answers with up-to-date market data, past examples of similar mergers, and insights from new articles. This way, when the AI answers the question about the merger, it doesn’t just give basic facts. Indeed, it goes deeper and explains what that merger could mean for the investor’s money.

b. Advantages of Knowledge Graphs Keeping a LLM Up-To-Date.

LLM by themselves only have knowledge since their last build. Below are the advantages of LLMs leveraging knowledge graphs to access up-to-date information.

• Improved Accuracy. If a Knowledge Graph is dynamically updated, an AI app has access to both up-to-date and reliable information. Moreover, Knowledge Graphs tells a story enabling LLMs to be more contextually aware to provide better responses to users.
• Reduced Hallucinations. Also with up-to-date Knowledge Graphs, LLM apps avoid “hallucinations” because their responses are grounded with the latest verifiable data and facts.
• More Adaptable. Lastly, Knowledge Graphs enable LLMs to quickly incorporate new information and updates. This is especially true for domains that need to stay current with the latest information such as ecommerce, healthcare, or financial services.

See Memgraph’s article, Why Knowledge Graphs Are the Ideal Structure for LLM Personalization, for a more detailed discussion on knowledge graphs that keep LLMs up-to-date.

“Knowledge Graphs tells a story enabling LLMs to be more contextually aware …”

3. Knowledge Graphs Enables LLMs to be Trustworthy by Aligning with Corporate Culture, Goals, and Wisdom.

Also, Knowledge Graphs enable organizations to better trust LLMs because the AI can provide explainable answers that align with organizational norms. By organizations linking LLM outputs to specific enterprise knowledge graph entities and relationships, LLMs become more transparent to the inner workings of their responses. Without a doubt, this transparency is essential for building trust in AI applications, particularly in high-stakes domains where accuracy and reliability are paramount.

a. Example of Knowledge Graphs Enabling LLM Answers to be More Trustworthy: Supply Chains

Knowledge Graph AI: The Best Uses For Successful Supply Chains

AI and Knowledge Graphs are a powerful combination for supply chain management, addressing the unique digital challenges of this complex business domain such as poor data quality and disjointed data. With integrated Knowledge Graphs, supply chain AI apps become more trustworthy and reliable. This is because LLMs accessing Knowledge Graph’s linked enterprise data enables AI apps to align with contextual enterprise information such as corporate policies and best operational practices. Hence, this enables AI to support better decision-making and even act as autonomous agents. Moreover, LLM responses can be trusted to better harmonize with organizational goals and norms.

For a more detailed discussion, see my article, Knowledge Graph AI: The Best Uses For Successful Supply Chains. This article includes 12 use cases across the supply chain from planning to procurement to supply chain operations to final delivery.

b. Advantages for LLMs to Rely on Knowledge Graphs to Increase Trustworthiness.

As discussed, enterprise-specific Knowledge Graphs gives LLMs access to organizational memory, policy awareness, and contextual structure. Thus, AI becomes more trustworthy as a tool that organizations can rely on. Specific advantages include:

• Can Explain Why. With Knowledge Graphs, there is the ability to trace the rationale behind AI outputs. Also, AI apps can explain how it reached conclusions based on relationships, constraints, and logic paths followed.
• Better Responses Based on Organizational Context. Additionally, AI apps can evaluate data and relationships in light of the organization’s historical transactions, organizational policy, and current conditions.
• Aligned with Enterprise Norms and Policies. Lastly with Knowledge Graphs, AI apps can better meet specific organizational requirements and norms. This is because the LLM does not need to rely solely on statistical likelihoods based on the training data.

For more detailed discussion on Knowledge Graphs and LLM trustworthiness, see TigerGraph’s article, Knowledge Graph LLM.

4. LLMs Can Rapidly Seed Knowledge Graph Development for Smarter Applications.

Lastly, organizations can use LLMs to significantly accelerate the development of enterprise ontologies and knowledge graphs. Today, most ontologies are developed and updated by committees, a long, drawn-out process. However, LLMs can streamline the building of ontologies and Knowledge Graphs. This is because LLMs, based on their training, already have embedded existing world models to help structure and organize enterprise data. Hence, LLMs have the capability to seed Knowledge Graph development, offering a more efficient development approach, saving both time and resources. While LLMs can’t replace manual ontology development entirely, they can effectively seed them, providing a solid foundation for further development and refinement.

“There’s a truth that traditional ontology communities are reluctant to face: Large language models already contain world models. They’re not formally axiomatized. They’re not neat. They’re not hand-built by committees. But they work.”

Kendall Clark

a. Example of a LLM Seeding an Enterprise Knowledge Graph:  Environmental Science.

To illustrate, let’s look at the task of creating a LLM-generated knowledge graph for a specific field such as environmental science. Here, a LLM can process thousands of scientific papers, extracting key concepts and their interrelations, such as pollution sources, ecosystem impacts, and conservation strategies. Thus, a LLM can effectively and rapidly create a knowledge graph that encapsulates the field’s collective understanding.

b. Advantages of Using LLMs in the Creation of Enterprise Knowledge Graphs

To list, below are the advantages of organizations using LLMs to seed ontologies for enterprise knowledge bases.

• Captures Knowledge Rapidly. First with limited human resources, enterprises can use LLMs to capture institutional knowledge from structured and unstructured data. Without a doubt, LLM seeding Knowledge Graphs is better than starting from scratch.
• Augments Domain Expertise. Also by using LLMs, enterprises can scale human domain expertise across multiple business units and use cases.
• Better Meet Regulatory Compliance Requirements With Less Resources. Additionally with LLM seeding Knowledge Graphs, there are more opportunities to have explainable AI systems to meet regulatory and compliance verification requirements.

For more references on using LLMs to seed enterprise knowledge graphs, see Kendall Clark’s article, Foundation Models Know Enough. Also, see EQT Ventures’ article, Knowledge Graph(s) and LLM-based ontologies have a very good shot at unlocking GenAI in production for the use of LLMs to help create knowledge graphs for specific enterprise needs.

More References on Knowledge Graph and LLM Synergism.

Below are more references on how knowledge graphs are making AI smarter.

• Jim Webber’s article, How knowledge graphs improve generative AI
• Kendall Clark’s article, Enterprise AI Requires the Fusion of LLM and Knowledge Graph
• Ingest AI’s article, Knowledge Graphs & LLMs: Multi-Hop Question Answering – Unlocking Intricate Text Understanding

Superior Knowledge Management: The Best Ways That Knowledge Graph AI Can Empower Businesses

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Need help with an innovative supply chain solution that leverages emerging information technologies? I’m Randy McClure, and I’ve spent many years helping logistics organizations to make the most of new information technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in new strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your supply chain or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.