The Role of Knowledge Graphs in Enhancing AI Accuracy

Artificial Intelligence (AI) can do astonishing things, like summarize complex data or generate creative content in seconds. Unfortunately, it also makes things up—popularly referred to as hallucinating. Hallucinations happen when an AI model outputs logical but inaccurate, even nonsensical, responses to prompts, which can undermine the model’s trustworthiness.

While these hallucinations are sometimes amusing, they may have serious repercussions, particularly in specialized fields like healthcare, finance, and law. According to Stanford University’s RegLab research, the likelihood of AI hallucination to verifiable legal queries can be anywhere from 69% to 88%.

Knowledge Graphs are specialized data structures that store information using a graph-based format. They provide additional semantic information between entities and their relationships, defining connections in a machine-readable and human-understandable format. Such an approach improves the performance of AI models like Large Language Models (LLMs). This blog explores how training models on diverse datasets alongside structured graph databases like knowledge graphs can enhance AI accuracy and reliability.

Challenges Faced by LLMs

Recent research highlights several challenges faced by Large Language Models (LLMs), especially regarding accuracy and performance inconsistencies. A recent study found that GPT-4 provided complete answers in only 53.3% of the cases.

Likewise, according to Vectara’s Hallucination Leaderboard, even the most popular LLMs like GPT, Llama, Gemini, and Claude hallucinate 2.5% to 8.5% of the time, with some models exceeding 15%.

The inaccuracy and inherent variability in responses mainly stems from three factors: the probabilistic nature of these models, aka ‘fuzzy matching,’ model drift, and semantic uncertainty.

Limitations of Fuzzy Matching

Fuzzy matching is a method that large language models (LLMs) use to generate responses by considering the probability of word or phrase connections rather than relying on exact matches. This allows LLMs to identify similarities between terms or concepts even if they aren’t identical, giving the model flexibility in interpreting and responding to various queries.

Despite its advantages, a major drawback of fuzzy matching is consistently delivering precise, context-specific information. The model depends on familiar patterns, and although it often generalizes effectively, it struggles with specialized or niche queries where accuracy is key. Furthermore, it can occasionally misunderstand the query’s intent, resulting in off-topic or misleading responses.

Model Drift

The concept of model drift worsens inconsistencies created by fuzzy matching over time. Model drift occurs because LLMs are trained on large datasets representing information at a specific point in time. As the world evolves—new facts emerge, social norms shift, and language usage changes—the training data may become outdated, causing the model’s predictions to drift.

Moreover, if an LLM is frequently fine-tuned or updated with new data, especially from user interactions, it might incorporate biases or misinformation. This can lead to errors or performance deviations, as the model may generate less reliable or relevant responses. When the data diverges from current knowledge, the LLM may fail to deliver accurate predictions or useful advice, reducing its trustworthiness and effectiveness.

Semantic Uncertainty

Detecting hallucinations in AI models can be challenging because of semantic uncertainty—the variability in how meaning can be expressed. A sentence can be rephrased in many ways while retaining the same core message. This makes it difficult to determine whether an AI’s response is genuinely accurate or a plausible-sounding hallucination.

For example, “France’s capital is Paris” and “Paris is France’s capital” express the same fact. The challenge arises when quantifying the model’s confidence in such cases.

Traditional approaches evaluate token-level probabilities—how likely the model thinks a specific word is correct—but don’t account for the broader meaning. As meanings can be expressed in various ways, evaluating semantic accuracy goes beyond just word choices.

Introduction to Knowledge Graphs and Their Benefits

Knowledge graphs offer a structured approach to representing knowledge by illustrating connections between various data points. These data points, or nodes, represent entities such as people, places, objects, or concepts. The edges between these nodes indicate relationships, which can be either direct or indirect.

Knowledge graphs empower systems to discern patterns and relationships within data through these structured representations. Unlike traditional databases that explicitly store relationships in rows and columns, knowledge graphs define relationships using flexible semantic links. This flexibility allows systems to infer connections that are not explicitly stored.

For instance, if a knowledge graph knows that a spoon is “part of” the cutlery, and the cutlery is “part of” the kitchen, the system can infer that the spoon is related to the kitchen, even without a direct connection.

This capacity to infer relationships allows knowledge graphs to derive new information without explicitly storing it. As a result, knowledge graphs can use this inferred data to become more versatile and interconnected than traditional databases.

Knowledge graphs enhance advanced analytics by storing additional information about how different sets of data are linked with each other. They integrate diverse data sources in advanced analytics to uncover complex relationships. For instance, linking patient records and research in healthcare can reveal treatment correlations. Likewise, knowledge graphs can also model relationships between biological entities, which makes it easier for AI models to predict drug interactions.

How Knowledge Graphs Can Improve LLM Accuracy and Reliability

Knowledge graphs significantly enhance the accuracy and reliability of AI models like LLMs by providing structured, context-rich data. According to a DataWorld study, integrating knowledge graphs can improve LLM accuracy by up to 300%. This is why a growing number of experts from across the industry, including academia, database companies, and industry analyst firms like Gartner, rely on? Knowledge Graphs to improve? LLM response accuracy.?

Here’s how knowledge graphs improve AI reliability and performance:

Providing Context Through Entity Relationships

Knowledge graphs map entities—such as people, places, concepts—and their relationships in a structured format. This allows LLMs to access rich contextual information. For example, in a biomedical knowledge graph, a “drug” could be linked to the “disease” it treats, the “genes” it targets, and related “clinical trials.” When LLMs use these structured relationships, they can deliver more accurate responses based on a deeper, contextual understanding of the data.

Disambiguation of Terms

One of the key challenges for LLMs is disambiguating terms that may have multiple meanings. Knowledge graphs address this by connecting terms to specific entities and contexts. For example, the word “placebo” might refer to a sugar pill or a saline injection. Knowledge graphs clarify this by linking “placebo” to the correct context—whether it’s “Sugar Pill in Clinical Trial” or “Saline Injection in Clinical Trial”—ensuring the LLM provides clear, unambiguous answers.

Semantic Enrichment of Data

Knowledge graphs enrich raw data by adding layers of meaning and linking it to relevant, structured information. For example, a knowledge graph in a clinical trial database can connect researchers, methodologies, and outcomes, allowing the LLM to better understand the relevance and interconnections between various data points. This semantic enrichment enhances the model’s ability to generate meaningful, data-driven insights.

Centralized Knowledge for Error-Free Responses

LLMs often draw on vast datasets that may include outdated or conflicting information. Knowledge graphs provide a single, structured, reliable reference point—often called a “single source of truth.” This eliminates discrepancies and ensures the model relies on accurate, consistent information.?

For example, in healthcare, knowledge graphs maintain consistency by ensuring that terms like “symptom,” “diagnosis,” and “treatment” are well-defined and interrelated. This helps reduce the risk of misinterpretation or error.

Enhanced Reasoning and Inference

LLMs sometimes struggle with logical reasoning or making inferences from information not directly present in their training data. Knowledge graphs fill this gap by providing logical, structured relationships between entities.?

For instance, if an LLM knows from a knowledge graph that “aspirin” is a treatment for “fever,” and “headache” is a common symptom of “fever,” it can infer that aspirin may also help treat a headache. This capacity for logical inference greatly enhances the model’s reliability in making accurate predictions.

Reducing Ambiguities in User Queries

Many user queries can be vague or ambiguous, but knowledge graphs help LLMs resolve these issues by linking terms to specific entities and relationships. For example, a query like “What were the clinical trial results for medication X?” can be answered precisely when the LLM references a knowledge graph. This graph contains details about the trial, its methodology, and outcomes, ensuring the response is accurate and based on well-structured data.

The Need to Detect LLM Hallucinations At Scale

Detecting hallucinations in AI is harder to identify and resolve compared to traditional software issues. Although regular human evaluation of LLM outputs and trial-and-error prompt engineering can aid in identifying and managing hallucinations within an application, this method is time-consuming and challenging to scale as the application expands.

Likewise, the growing volume of generated data and the demand for real-time responses make it difficult to detect hallucinations. Manually reviewing each output is impractical, and the varying levels of human expertise make the process inconsistent. In high-stakes fields such as healthcare and finance, where inaccuracies can have grave consequences, relying solely on human review is both slow and prone to errors.

Although automated tools designed to detect hallucinations exist, they often depend on analyzing sentences or phrases to comprehend context and identify inaccuracies. This method can be effective, yet it frequently struggles to capture intricate details or recognize subtle inconsistencies and inaccuracies. Due to a limited understanding of semantic relationships between entities, traditional hallucination detectors often fall short in analyzing complex or nuanced content.

How Pythia Enhances AI Accuracy Using A Billion-Scale Knowledge Graph

Wisecube’s Pythia offers an innovative way to tackle a major issue in AI: unreliable information. With a unique set of tools, Pythia enhances AI accuracy while significantly reducing errors from large language models (LLMs). Here’s a breakdown of the key components driving Pythia’s solution:

Most AI systems detect errors or “hallucinations” by reviewing complete sentences or phrases. However, this often misses the smaller, more crucial details. Pythia goes further by introducing “knowledge triplets,” which break down AI-generated claims into a structured format: <subject, predicate, object>.

This approach makes it easier for the AI to grasp the relationships between entities, leading to more precise and context-aware responses. For example:

• Subject: Jake McCallister
• Predicate: Received
• Object: COVID-19 vaccination

Instead of just focusing on keywords like “COVID-19 vaccination,” Pythia’s method captures the action (received) and what exactly happened (COVID-19 vaccination). This level of detail is critical in ensuring AI accuracy.

Real-Time Hallucination Detection

One of the most significant challenges with LLMs is their tendency to generate realistic but factually incorrect information (hallucinations). Pythia addresses this through its real-time hallucination detection module, which identifies and flags such errors immediately.?

Pythia ensures that only factually accurate information makes it through the system by using a combination of natural language inference (NLI), large language model checks, and knowledge graph validation. As a result, organizations can detect misleading responses and ensure the overall trustworthiness of AI-generated outputs.

Semantic Data Transformation for Better Context Understanding

Pythia transforms raw data into the Resource Description Framework (RDF) format, enabling LLMs to interpret data in a more meaningful way. This transformation captures the relationships between data points and structures them semantically, providing LLMs with deeper context for understanding and generating responses. By grounding the AI’s insights in a semantic data model, Pythia enhances the model’s ability to deliver contextually rich and accurate outputs that align with real-world facts.

Knowledge Graph: The Validation Engine Behind the Scenes

At the heart of Pythia’s solution is a vast knowledge graph built for advanced fact-checking. With access to millions of publications and billions of data points, Pythia ensures that AI-generated claims are fact-checked against a massive pool of verified information.

Pythia helps the AI detect and avoid false or misleading information by mapping out relationships between key facts in real-time. It also helps avoid errors or hallucinations arising from AI fabricating information by cross-referencing LLM outputs with verified data. This factual validation is beneficial in domains like healthcare, where accuracy is non-negotiable.

Claim Extraction and Categorization

Pythia uses an advanced claim extraction and categorization system to maintain factual accuracy. This feature compares LLM-generated responses against established knowledge bases, classifying claims into four categories:

• Entailment (accurate claims)?
• Contradictions (hallucinations)?
• Missing Facts
• Neutral claims

Pythia provides a clear pathway for improving LLM outputs by flagging contradictions and missing facts. This helps developers address knowledge gaps and eliminate inconsistencies.

Schema Mapping and Relationship Capture

The accuracy of an LLM depends on the data it processes and how well it understands the relationships between different data points. Pythia’s schema mapping bridges the gap between various data sources and standardized ontologies, ensuring that complex relationships within datasets are properly captured.?

This deeper understanding of data interconnections enables the LLM to produce more accurate insights and deliver reliable and relevant results to the task at hand.

Continuous Monitoring and Alerts

Accuracy in LLMs isn’t just about improving the model itself and maintaining high standards during real-time operations. Pythia’s continuous monitoring tracks LLM performance, gathering metrics and raising alerts whenever discrepancies or anomalies are detected. These alerts keep operators informed. They allow immediate action when accuracy thresholds are breached, preventing erroneous outputs from affecting end users.

Input and Output Validation

Pythia’s input and output validators add another layer of accuracy assurance by validating both user prompts and LLM responses. Input validators ensure that only complete, relevant, and high-quality data enters the system, preventing “garbage-in, garbage-out” scenarios. Meanwhile, output validators assess the AI’s responses for logical inconsistencies, bias, gibberish, toxic language, and factual correctness, ensuring that only high-quality and reliable outputs are delivered.

Task-Specific Accuracy Metrics

Different tasks require different standards of accuracy. Pythia enhances LLM accuracy by implementing task-specific metrics and assigning weights to claims based on their relevance to the query. This ensures that the AI focuses on providing the most pertinent and factually correct information for each specific use case, be it a biomedical question or a financial analysis.

Custom Dataset Integration

Pythia enables the integration of custom datasets into its pipeline. This allows LLMs to be fine-tuned for domain-specific knowledge. Whether it’s healthcare, law, or finance, custom dataset integration helps ensure the AI’s responses align with industry-specific facts and standards.

Final Words

Integrating knowledge graphs into AI frameworks enhances the accuracy of LLMs by adding a crucial layer of verification and context between data sources. With greater validation, organizations can significantly reduce errors and lower the risk of hallucinations, leading to more reliable, context-aware decision making.

Pythia takes this concept further by seamlessly integrating LLMs with a billion-scale knowledge graph. Through techniques like knowledge triplets and real-time monitoring, Pythia improves AI accuracy and ensures outputs are both precise and contextually relevant.

Get in touch with us today and learn more about how Pythia uses knowledge graphs for optimized hallucination detection.

The article was originally published on Wisecube's website.