Building the next generation of AI: In-depth exploration of the integration method of knowledge graph KG and large model LLM

In today’s AI environment, there are two key technologies that are transforming machine understanding, reasoning, and natural language processing: Large Language Models (LLMs) and Knowledge Graphs (KGs).

LLMs, such as OpenAI’s GPT series or Meta’s Llama series, show incredible potential in generating human-like text, answering complex questions, and creating content across different domains.

At the same time, KGs help organize and integrate information in a structured way, enabling machines to understand and infer relationships between real-world entities. They encode entities (such as people, places, and things) and the relationships between them, making them ideal for tasks such as question answering and information retrieval.

The Evolution of AI: Big Model Revolution Powered by Knowledge Graph

Exploration of a new paradigm of industry-university-research cooperation in compliant medicine big model enhanced by knowledge graph

Emerging research shows that the synergy between LLMs and KGs can help us create AI systems that are more context-aware and accurate. In this article, we explore different ways to integrate the two, showing how this can help you leverage the strengths of both.

Knowledge Graph and LLM Integration Method

You can think of the interaction between LLM and KG in three main ways.

First, there are knowledge-augmented language models, where the KG is used to enhance and inform the capabilities of the LLM. Second, you have LLM-for-KGs, where the LLM is used to enhance and improve the capabilities of the KG. Finally, there are hybrid models, where the LLM and KG work together to achieve more advanced and complex results.

Let’s look at these three.

1. Knowledge-enhanced language model (KG-enhanced LLM)

A straightforward way to integrate a KG with an LLM is through a Knowledge Augmented Language Model (KALM). In this approach, you augment your LLM with structured knowledge from a KG, allowing the model to base its predictions on reliable data. For example, KALM can significantly improve the following tasks:

Named Entity Recognition (NER)

Accurately identify and classify entities in text by using the structured information in the KG. This approach allows you to combine the generative power of LLM with the precision of KG, resulting in a model that is both generative and accurate.

A straightforward way to integrate a KG with an LLM is through a knowledge-augmented language model (KALM). In this approach, you augment your LLM with structured knowledge from a KG, allowing the model to base its predictions on reliable data. For example, a KALM can significantly improve  tasks such as named entity recognition (NER) by using the structured information in the KG to accurately identify and classify entities in text . This approach allows you to combine the generative power of an LLM with the precision of a KG, resulting in a model that is both robust and accurate.

2. KG's LLM

Another approach is to use Large Language Models (LLMs) to simplify the creation of Knowledge Graphs (KGs). LLMs can assist in creating Knowledge Graph ontologies. You can also use LLMs to automatically extract entities and relations. In addition, LLMs help complete KGs by predicting missing components based on existing patterns, as shown in models such as KG-BERT. They also ensure the accuracy and consistency of the KG by validating and checking the information against a corpus.

3. Hybrid model (LLM-KG collaboration)

Hybrid models represent a more complex integration where the KG and LLM collaborate throughout the process of understanding and generating responses. In these models, the KG is integrated into the reasoning process of the LLM.

One such approach is to post-process the output generated by the LLM using a knowledge graph. This ensures that the responses provided by the model are consistent with the structured data in the graph. In this case, the KG acts as a validation layer, correcting any inconsistencies or inaccuracies that may have occurred during the LLM generation process.

Alternatively, you can build an AI workflow to create LLM prompts by querying the KG for relevant information. This information is then used to generate responses, which are finally cross-checked against the KG to ensure accuracy.

Benefits of integrating knowledge graph and LLM

There are many benefits of integrating LLM with knowledge graphs. Here are some of them.

1. Enhanced data management

Integrating a KG with an LLM lets you manage your data more efficiently. A KG provides a structured format for organizing information, which the LLM can then access and use to generate intelligent responses. A KG also allows you to visualize the data, which you can use to identify any inconsistencies. Few data management systems offer the flexibility and simplicity that a KG provides.

2. Situational Understanding

By combining the structured knowledge of KGs with the language processing capabilities of LLMs, you can gain a deeper contextual understanding of your AI systems. This integration allows your models to use the relationships between different information and helps you build explainable AI systems.

3. Collaborative knowledge building

KG-LLM integration also helps create systems where the KG and LLM improve each other. As the LLM processes new information, your algorithm can update the KG with new relationships or facts, which in turn can be used to improve the performance of the LLM. This adaptive process ensures that your AI system continuously improves and stays up to date.

4. Dynamic Learning

By leveraging the structured knowledge provided by KGs, you can build LLM-driven AI systems in fields such as healthcare or finance, where data is dynamic and evolving. Keeping your KGs continuously updated with the latest information ensures that LLMs have access to accurate and relevant context. This enhances their ability to generate precise and contextually appropriate responses.

5. Improve decision making

One of the most notable benefits of integrating KGs with LLMs is enhanced decision-making. By basing its decisions on structured, reliable knowledge, your AI system can make more informed and accurate choices, reducing the potential for errors and hallucinations and improving overall outcomes. An example of this is the GraphRAG system, which is increasingly being used to enhance LLM responses with factual, informed data that was not part of its training dataset.

1. Consistency and consistency

One of the main challenges you may face when integrating a KG with an LLM is ensuring consistency and consistency between the two. Since a KG is structured, while an LLM is more flexible and generative, it can be challenging to align the output of the LLM with the structure and rules of the KG. To ensure that the two systems work together, you need to create a mitigator that is responsible for prompting the LLM and issuing KG queries when the LLM requires additional context.

2. Real-time query

Another challenge is real-time querying. While KGs can provide highly accurate and structured information, querying them in real time can be computationally expensive and time-consuming. This can be a significant hurdle if you want to integrate your KG with an LLM for real-time applications, such as chatbots or virtual assistants. To go beyond this, you should ensure that your KG is low-latency and highly scalable.

3. Scalability

As data grows, so does the complexity of managing the KG and integrating it with the LLM. Scalability is a major challenge because both the KG and the LLM need to be updated and maintained as new information emerges. This requires you to build infrastructure to scale and use scalable techniques to support KG queries.

4. Dealing with illusions and inaccuracies

While KGs can help alleviate the problem of hallucinations in LLMs, eliminating hallucinations remains a challenge. Ensuring that your LLMs only produce factually accurate information requires ongoing validation and improvement of both the model and the KG. Additionally, addressing inaccuracies in the KG itself can further complicate the integration process. Where possible, you should use KG visualization tools to inspect and improve your KG data.

How LLM helps in the knowledge graph creation process

LLMs play an increasingly important role in the creation of knowledge graphs. LLMs such as the GPT-4o family, the Cohere model, the Gemini model, or variants of Llama 3.1 and Mistral have demonstrated a strong ability to detect entities and relations from natural language texts and can be used to create KGs.

By choosing the right LLM, you can automatically extract entities and relationships from your data, ensuring that the graph you create is comprehensive, accurate, and continuously updated. This is especially valuable in fields where data is constantly evolving, making manual updates to the knowledge graph tedious or difficult.

• Entity resolution

  LLM can significantly enhance the entity resolution process in KG creation by accurately identifying and linking entities in different data sources. This can help you create more consistent and reliable KGs, thereby improving the performance of AI systems.

  
  

• Unstructured data labeling

  By leveraging the language understanding capabilities of LLM, you can effectively tag unstructured data with relevant entities and relations. This makes it easier to integrate unstructured data into KGs, thereby expanding its scope and utility.

  
  

• Entity and class extraction

  LLM can efficiently extract entities and classes from a dataset. This step is critical to creating a comprehensive KG. By automating this process, you can reduce the time and effort required to build and maintain a KG.

  
  

• Body alignment

  LLM can help align different ontologies in a KG and ensure the consistency and logic of the relationships between entities. This consistency is crucial to maintaining the integrity and accuracy of the KG as it evolves.

Using LLM to enhance knowledge graph creation

To simplify the creation of the KG, you can use a Large Language Model (LLM), such as the GPT-4o model. You can hint the LLM in the following ways:

“Extract key entities and relationships from the following text and generate corresponding Cypher queries:

In one study, Dr. Alice Johnson of Harvard University tested drug Y in Phase II of the trial and showed significant improvement in patient outcomes.

“Extract key entities and relations from the following text and generate corresponding Cypher queries: In a study, Dr. Alice Johnson of Harvard University tested drug X for disease Y during Phase II of the trial and showed significant improvement in patient outcomes.”

MERGE (d:Drug {name: "Drug improvement"})MERGE (d)-[:TREATS]->(disease)MERGE (person)-[:STUDIED_BY]->(d)MERGE (d)-[:PART_OF]->(phase)MERGE (person)-[:ASSOCIATED_WITH]->(institution)MERGE (d)-[:RESULTED_IN]->(outcome)

As new data comes in, you can prompt the LLM to extract new entities and relations and update the KG.

For example, if the new input is:

“A subsequent Phase III study of Drug X, led by Dr. Bob Smith at Stanford University, confirmed the earlier findings.”

The generated Cypher query will be:

MERGE (d:Drug {name: "Drug (person)-[:ASSOCIATED_WITH]->(institution)

Therefore, using LLM can significantly reduce the time required to create a KG. You can also automatically update the KG using queries generated by LLM.

Improving LLM performance using knowledge graphs

Alternatively, integrating a knowledge graph (KG) into a large language model (LLM) can significantly improve its performance. In this approach, you query the KG to retrieve relevant context before using the LLM to generate a response.

For example, if you are developing a healthcare chatbot and need to answer questions about drug interactions, you can first query the KG using a Cypher match query:

// Query to find interactions of Drug XMATCH (drug:Drug {name: "Drug X"})-[:INTERACTS_WITH]->(interaction:Drug)RETURN interaction.name AS InteractingDrugs

After obtaining relevant data from the KG, it is incorporated into the LLM prompt. This approach helps the model generate more accurate and contextually relevant responses:

import openai# Assume you retrieved the following data from the KGinteracting_drugs = ["Drug Y", "Drug Z"]# Create a prompt using KG dataprompt = f"""A patient is taking Drug X. What should be considered in terms of drug interactions?Note that Drug X interacts with {', '.join(interacting_drugs)}."""# Call the LLM with enhanced promptresponse = openai.ChatCompletion.create( model="gpt-4o", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ])print(response.choices[0].message['content'])

Finally, as the LLM processes more queries and provides new information, you can update the KG to reflect the new knowledge. For example, if the LLM correctly identifies a new drug interaction, you can insert this relationship into the KG:

// Update the KG with new interactionMERGE (drug:Drug {name: "Drug X"})MERGE (new_interaction:Drug {name: "Suggested Drug"})MERGE (drug)-[:INTERACTS_WITH]->(new_interaction)

This continuous learning loop allows the LLM and KG to evolve together and keep your AI system up to date.

How LLM retrieves information from the knowledge graph and its accuracy

1. Search methods

LLM can retrieve information from KG using a variety of methods, including direct query, embedding, and graph traversal algorithms. These methods allow LLM to access the structured knowledge in the KG and use it to inform its output.

2. Coefficient of Precision

The accuracy of the information that LLM retrieves from the KG depends on several factors, including the quality of the KG, the retrieval method used, and the context in which the information is applied. By optimizing these factors, you can improve the accuracy of LLM output.

3. Accuracy of retrieved information

Ensuring the accuracy of the information that the LLM retrieves from the KG is critical to maintaining the reliability of your AI system. LLMs and KGs must be regularly validated and improved to ensure that your AI system provides accurate and contextually relevant information.

How the knowledge graph + big model dual-wheel drive solution can help

When you want to integrate knowledge graphs (KGs) with large language models (LLMs) for advanced AI applications, graph databases provide cutting-edge and scalable solutions designed to meet the needs of high-performance, real-time knowledge retrieval. It also provides graph visualization, which simplifies the process of building and managing graph data.

When you want to integrate knowledge graphs (KGs) with large language models (LLMs) for advanced AI applications, it provides a cutting-edge and scalable solution designed to meet the needs of high-performance, real-time knowledge retrieval. It also provides graph visualization, simplifying the process of building and managing graph data.

Furthermore, the GraphRAG (Graph Retrieval Augmented Generation) feature ensures that LLM not only retrieves the most accurate information but also generates context-rich and factually correct output. In addition, it supports KG queries and vector searches, which are very beneficial for building applications in domains where accuracy and precision are critical.

in conclusion

The integration of Large Language Models (LLMs) with Knowledge Graphs (KGs) represents a huge advance in AI, enabling systems to combine the natural language processing benefits of LLMs with the structured relational data stored in KGs. This synergy enables enhanced contextual understanding, dynamic learning, and improved decision-making, making your AI applications more robust and accurate.

However, integrating these technologies is not without challenges. Issues such as alignment and consistency, real-time queries, scalability, and hallucination handling need to be carefully managed. To address these challenges and maximize the benefits of LLM and KG integration, a compelling solution is provided.

#BigModel#KnowledgeGraph#KG#LLM#Fusion#GenAI