Leveraging the Power of Knowledge Graphs: Enhancing Large Language Models with Structured Knowledge

Leveraging the Power of Knowledge Graphs: Enhancing Large Language Models with Structured Knowledge

Within the constantly shifting field of artificial intelligence (AI), Large Language Models (LLMs) have come to prominence, astonishing us with their language processing capabilities. The most prominent of which is ChatGPT by OpenAI, with its interactive and contextually rich conversations. Models like ChatGPT excel in fostering creativity, tailoring personalized recommendations, assisting in writing tasks, and contributing to building predictive models. Other popular LLMs include Google’s LaMDa and open-source LLMs like BLOOM and Meta’s LLaMA.

These models can generate human-like text, primarily operating within unstructured data – a vast expanse of information devoid of explicit organization. However, as LLMs continue to push the boundaries of language processing, a conspicuous gap has surfaced – their inherent struggle to integrate seamlessly with structured data.

In this article, we will highlight the increasing gap between LLMs and structured information. Furthermore, we uncover the potential for bridging this gap by harnessing the capabilities of knowledge graphs. Lastly, we will discuss how a relationship between LLMs and structured knowledge can enhance real-world applications of LLMS.

Significance of Structured Data for LLMs

Structured Data

Structured data refers to information that is systematically arranged into a predefined format, typically using tables, databases, or schemas. This organization facilitates the efficient storage and retrieval of information and the establishment of clear relationships between different data points. Unlike unstructured data, where information is presented freely, the standardization of structured data enables quick and accurate access to specific pieces of information.

Structured data presents complex relationships and dependencies among different data elements in an organized manner. This organized structure paves the way for consistent and unambiguous interpretations by LLMs, as it aligns with their ability to identify patterns and relationships in data. This is particularly advantageous when dealing with domains that require precision, such as scientific research, financial analysis, or medical diagnostics.

Importance of Enriching LLMs with Structured Data

Structured data plays a pivotal role in enhancing the capabilities of large language models. Some of the benefits of combining structured data and LLMs include:

Enhanced Context

LLMs generate text by utilizing the patterns they have learned from their training data. Incorporating structured data enhances the context in which these models operate. LLMs can extract and utilize contextual information more effectively when they have access to structured data. This, in turn, results in more accurate and relevant text generation. 

For instance, a medical LLM can provide detailed explanations about a specific disease by drawing upon structured medical databases, leading to more precise and contextually informed responses. To explain this, we prompted ChatGPT to answer, “What genes are associated with Autism Spectrum Disorder (ASD)?. Here’s how it responded to its limited context of this complicated disorder:

Without a structured medical database, ChatGPT cannot understand the context beyond the direct association of x number of genes with ASD. Structured knowledge about ASD can help improve this response by providing a comprehensive schema context and examples of ASD to ChatGPT. This will enable ChatGPT to grasp the intricate relationships between not only the genes but also various subtypes of ASD.

Improved Credibility

Hallucination refers to the phenomenon where LLMs generate information that sounds plausible but is not grounded in actual data. The incorporation of structured data acts as a safeguard against this issue. By cross-referencing generated content with structured data, LLMs can verify the accuracy and validity of the information they produce. This validation mechanism reduces the likelihood of LLMs generating misleading or incorrect statements, enhancing their credibility and utility. Consider, for example, an LLM offering a treatment recommendation based on fictional symptoms not present in established medical databases. By incorporating structured medical data, the LLM can cross-reference its responses, reducing the risk of disseminating incorrect medical advice and ensuring a higher level of reliability in its outputs.

Knowledge Graphs as a Source of Structured Information 

Knowledge graphs are a type of structured data representation that capture the relationships and connections between real-world entities and concepts. They organize information in a graph-like structure, where nodes symbolize entities (such as people, organizations, products, places, and concepts), and edges denote the relationships between these entities. Knowledge graphs aim to provide a comprehensive understanding of complex domains by modeling not only the individual data points but also the intricate associations that exist among them.

Knowledge graphs serve as exceptional tools for querying structured data due to their intrinsic design and capabilities:

  • Semantic Context: By incorporating semantic context through interconnected nodes and relationships, knowledge graphs capture nuanced meanings often lost in traditional tabular databases. This results in querying that produces accurate, contextually rich outcomes.
  • Flexibility: Their flexibility in accommodating diverse and evolving relationships is invaluable for representing complex interdependencies among data points in various scenarios.
  • Inferencing: Knowledge graphs support inferencing, deriving implicit relationships from existing data, thus enabling users to receive logical insights beyond explicit information.
  • Cross-domain Insights: Integrating data from multiple domains, these graphs provide a comprehensive view of interconnected information, enhancing the scope for querying and analysis.

Ways of Using Structured Data with LLMs

Incorporating structured data into LLMs introduces diverse approaches that harness the power of organized information for enhanced text generation. Let’s explore the various strategies you can use to leverage structured data for enhancing LLMs:

Directly Combining Structured Data

One straightforward approach involves merging structured data directly with LLMs. One can accomplish this by formatting the structured data into CSV or RDF files. By feeding these files as supplementary inputs alongside textual prompts, LLMs gain access to structured information during text generation. For instance, when composing a news article, an LLM can utilize CSV-based financial data to provide up-to-date context on market trends.

Using Knowledge Graphs for In-Context Learning

Leveraging knowledge graphs involves two key steps: constructing a knowledge graph from structured data and then utilizing this graph for in-context input prompting in LLMs.

In the first step, structured data is transformed into a knowledge graph that captures relationships and information. In the second step, relevant knowledge graph fragments are embedded within input prompts for LLMs, enabling them to generate text aligned with the structured data’s context. This technique facilitates in-context learning, empowering LLMs to create coherent and precise text that reflects the underlying structured information.

Knowledge Graphs as Context For LLMs (Source: Beyond ChatGPT in Medicine)

Consider constructing a knowledge graph for diabetes that captures data about symptoms, causes, and treatments. In the next step, when the LLM is prompted to explain diabetes symptoms, a snippet from the constructed knowledge graph describing common symptoms like frequent urination and fatigue is included in the input. This primes the LLM to generate a response that seamlessly blends its language understanding with the factual insights from the knowledge graph, resulting in accurate and contextually relevant information.

Utilizing Raw Metadata and Knowledge Graphs in Tandem

The strategy of using raw metadata and knowledge graphs together involves integrating additional details from raw metadata, such as publication dates and author names, with relevant segments from knowledge graphs.

For instance, when generating medical case summaries, LLMs can incorporate patient metadata alongside pertinent knowledge graph elements for a broader medical context.

Benefits of Leveraging Knowledge Graphs For Providing Structured Data to LLMs

Leveraging knowledge graphs to offer structured data to LLMs hold great potential to enhance the quality and reliability of generated content. Here is how this collaboration can benefit LLMs:

  • LLM Validation: Knowledge graphs act as validators for LLM-generated text. By cross-referencing responses with structured data, LLMs can ensure that their outputs align with accurate and validated information, reducing the risk of disseminating incorrect or misleading content.
  • Coherent and Accurate Responses: Knowledge graphs facilitate the integration of factual information into LLM-generated text. This synergy results in responses that seamlessly blend coherent language generation with the accuracy and precision of structured data, delivering well-rounded and reliable content.
  • Information Relevance: Knowledge graphs enhance the contextual relevance of LLM responses. By embedding relevant graph snippets into input prompts, LLMs can produce text contextually aligned with the structured data, resulting in information that resonates more effectively with users looking for domain-specific answers.
  • Enhanced Precision: Structured data from knowledge graphs loads LLM-generated content with enhanced precision. As knowledge graphs capture intricate relationships and dependencies, LLMs can tap into this depth of understanding to produce not only linguistically sound but also factually accurate text.

Real-World Applications of Using Knowledge Graphs to Empower LLMs

The utilization of knowledge graphs to empower large language models finds practical applications across diverse domains. Two notable instances include:

  • Enriched Search Engine Results: Incorporating knowledge graphs into search engines enables LLMs to comprehend user queries and context better. Google’s Knowledge Graph, for instance, not only provides relevant search results for a query like “famous artists,” but it can also offer related information about their notable works, historical context, and artistic influences, enriching the user’s understanding.
  • Improved Healthcare Diagnostics: Knowledge graphs integrated with structured medical databases enhance LLMs’ ability to provide accurate and contextually relevant healthcare information. For example, integrating an LLM like GPT with Wisecube’s biomedical knowledge graph can assist biomedical researchers in getting a deeper understanding of disease symptoms, conditions, and treatment options, streamlining the diagnostic process.

How Wisecube Unifies Knowledge Graphs and LLMs

The integration of large language models and knowledge graphs represents a transformative stride in the realm of natural language processing and knowledge representation. This integration advances the understanding of language and enriches the potential for structured data utilization. As these techniques progress and mature, they hold the key to unlocking elevated language comprehension and facilitating groundbreaking knowledge discovery.

At Wisecube, we are at the forefront of harnessing this potent collaboration. Our biomedical knowledge graph contains vast biomedical literature and cutting-edge medical information. Through seamless collaboration with GPT-4, Wisecube reshapes the biomedical landscape with its robust AI technologies.

If you’re eager to advance your biomedical analytics, connect with us today to explore the transformative potential of Wisecube’s Knowledge Graph Engine alongside GPT-4.

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