By using GraphRAG, we can achieve a bidirectional communication between Knowledge Graphs and LLMs, where both sides can benefit from each other’s strengths and compensate for each other’s weaknesses. We can also enable a more natural and intuitive interaction between humans and machines using natural language and structured data.
Knowledge Graphs are a powerful tool for representing and querying structured data. They can capture the relationships and attributes of entities, such as people, places, events, products, etc. Knowledge Graphs can also enrich the data with external sources, such as Wikipedia, DBpedia, or other domain-specific ontologies.
However, Knowledge Graphs have some limitations when it comes to dealing with natural language data, such as text documents, social media posts, or conversational transcripts. Natural language data is often unstructured, ambiguous, noisy, and incomplete. It may contain implicit information that is not explicitly stated in the text, such as opinions, emotions, intentions, or preferences. It may also contain references to entities that are not present in the Knowledge Graph, such as new or emerging concepts, slang terms, or named entities.
LLM to the Rescue
This is where Large Language Models (LLM) come in. LLMs are neural network models that learn to generate natural language text based on a given context. LLMs can capture the syntactic and semantic patterns of natural language, as well as the common sense and world knowledge that are implicit in the text. LLMs can also generate novel and diverse texts that are coherent and fluent.
However, LLMs also have some limitations when it comes to generating high-quality and relevant texts. LLMs may generate texts that are factually incorrect, inconsistent, or irrelevant to the context. LLMs may also generate texts that are biased, offensive, or harmful to certain groups of people. LLMs may also lack domain-specific knowledge or terminology that are required for certain tasks or applications.
Knowledge Graph to the Rescue
This is where Knowledge Graphs and LLMs can work together to create a powerful synergy. By combining Knowledge Graphs and LLMs, we can leverage the strengths of both approaches and overcome their weaknesses. We can use Knowledge Graphs to provide structured and factual information to LLMs, and use LLMs to provide natural language generation and understanding capabilities to Knowledge Graphs.
KG & LLM in practice
In this blog post, we will introduce a framework for using Knowledge Graphs with LLMs (GraphRAG), and show some examples of how it can be applied to various tasks and domains. We will also discuss some of the challenges and opportunities for future research in this area.
Question answering:
GraphRAG can use Knowledge Graphs to retrieve relevant facts and entities related to a natural language question, and use LLMs to generate a natural language answer that is concise and accurate.
Text summarization:
GraphRAG can use Knowledge Graphs to extract key information and concepts from a long text document, and use LLMs to generate a short summary that captures the main points and highlights.
Text generation
GraphRAG can use Knowledge Graphs to provide a rich context and background for generating natural language texts on a given topic or domain, and use LLMs to generate texts that are informative, creative, and engaging.
What else can KG & LLM achieve?
GraphRAG is not the first approach that tries to combine Knowledge Graphs and LLMs. There are several existing methods that use Knowledge Graphs as an input or a source of information for LLMs. For example:
Entity Linking:
This method identifies and links the entities mentioned in a natural language text to their corresponding nodes in a Knowledge Graph. This can help LLMs to disambiguate the meaning of the entities and provide more accurate and relevant texts.
Entity Embedding:
This method learns a vector representation for each entity in a Knowledge Graph based on its attributes and relationships. This can help LLMs to incorporate the semantic information of the entities into their text generation process.
Knowledge Injection:
This method injects relevant facts or information from a Knowledge Graph into the natural language text generated by an LLM. This can help LLMs to enhance the quality and diversity of their texts.
GraphRAG can do much more
However, GraphRAG goes beyond these methods by using Knowledge Graphs not only as an input or a source of information for LLMs, but also as an output or a target of information for LLMs. In other words, GraphRAG uses LLMs not only to generate natural language texts from Knowledge Graphs, but also to generate Knowledge Graphs from natural language texts. For example:
Knowledge Extraction:
This method extracts facts or information from a natural language text and converts them into triples or nodes and edges in a Knowledge Graph. This can help Knowledge Graphs to expand their coverage and update their data with new or emerging information.
Knowledge Completion:
This method predicts missing facts or information in a Knowledge Graph based on the existing facts or information and the natural language context. This can help Knowledge Graphs to fill in the gaps and improve their completeness and consistency.
Knowledge Refinement:
This method corrects erroneous or outdated facts or information in a Knowledge Graph based on the natural language evidence or feedback. This can help Knowledge Graphs to maintain their quality and accuracy.
Summary
By using GraphRAG, we can achieve a bidirectional communication between Knowledge Graphs and LLMs, where both sides can benefit from each other’s strengths and compensate for each other’s weaknesses. We can also enable a more natural and intuitive interaction between humans and machines using natural language and structured data.
