Combining Large Language Models and Knowledge Graphs

Featured Blog Image-Synergizing Large Language Models and Knowledge Graphs

In recent years, significant advancements in artificial intelligence (AI) have expedited the development of large language models and knowledge graphs, revolutionizing how we process and understand information.

Large language models, such as OpenAI’s GPT-4, have demonstrated exceptional capabilities in generating human-like text and performing various language-related tasks. On the other hand, knowledge graphs have emerged as powerful tools for organizing and representing structured knowledge, enabling efficient data retrieval and inference. Both these technologies have demonstrated immense potential in isolation, but the true transformative power lies in their integration.

The unification of large language models and knowledge graphs presents a compelling opportunity to enhance the capabilities and intelligence of AI systems. Combining the generative power of large language models with the semantic richness and structured representation of knowledge graphs can build more context-aware, accurate, and explainable AI systems that revolutionize how we interact with and leverage information.

In this article, we will discuss the various shortcomings of knowledge graphs and large language models and how they can be addressed by combining the two. We will also explore the different ways to combine knowledge graphs and large language models and discuss the scenarios they can be applied to.

Exploring Large Language Models

Background

Large Language Models (LLMs) are powerful artificial intelligence models pre-trained on large-scale data to understand and generate human language. They have recently gained significant attention and popularity due to their ability to perform various natural language processing (NLP) tasks, such as text completion, translation, summarization, and question-answering.

Types of Large Language Models

Large language models are transformer-based models that leverage a self-attention mechanism to process text using encoder-decoder modules. The encoder block processes input text and produces numerical representations called embeddings that capture the context and meaning of the text. The decoder block takes these embeddings as input and analyzes them to generate meaningful and relevant output sequences of text.

Based on the different architectural structures of their underlying transformer models, LLMs can be divided into the following three categories:

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  1. Encoder-only LLMs leverage only the encoder to process sequential text input to understand the contextual relationships between words. These LLMs are most suitable for tasks requiring the interpretation of individual words in a complete sentence, e.g., sentiment analysis, text classification, and named entity recognition. For example, BERT, RoBERTa, and ELECTRA are encoder-only models.
  2. Decoder-only LLMs use only the decoder module to generate human-like language output. These models are trained to predict the next word given the previous context, producing coherent and relevant output. They are often used in downstream tasks like text generation, machine translation, and image captioning. For example, ChatGPT and GPT-4 are built on a decoder-only architecture.
  3. Encoder-decoder LLMs combine the strengths of both encoder and decoder models. This architecture enables the model to perform tasks like text summarization and question-answering, where input text is encoded for context and then decoded to generate relevant output. An example of an encoder-decoder LLM includes a bilingual model called GLM130B.

Strengths of Large Language Models

Here are the several strengths that highlight the capabilities of large language models:

  • Generalizability: Large language models are trained on vast amounts of diverse text data, allowing them to generalize well to different domains, topics, and writing styles. They can effectively handle a variety of language-related tasks without the need for extensive task-specific training.
  • General Knowledge: LLMs can act as valuable resources for accessing general knowledge. They assist with research and satisfying information curiosity by providing accurate and informative responses based on the wealth of information they have been exposed to during training. This makes them efficient for tasks such as question-answering, knowledge expansions, information synthesis, and more.
  • Language Processing: Large language models are powerful tools for various tasks and applications involving language processing, generation, and adaptation. They can accurately perform complex language processing tasks by interpreting and analyzing the semantic and syntactic sentence structures. These versatile language models find effective usage in natural language understanding, sentiment analysis, text classification, and information extraction tasks.

Weaknesses of Large Language Models

While large language models possess remarkable strengths, it is essential to acknowledge their limitations and weaknesses:

  • Hallucination: Large language models may generate outputs that sound plausible but offer factually incorrect information. This phenomenon, known as hallucination, can occur when the models over-generate or make assumptions based on incomplete or inaccurate data.
  • Black-box Nature: Large language models are often perceived as black boxes, making it challenging to understand the internal mechanisms that drive their decision-making. This lack of interpretability can raise concerns about accountability, trust, and the potential for biased or undesirable outputs.
  • Indecisiveness: Large language models leverage probability models for reasoning. The indecisive nature of these models can affect the ability of LLMs to make decisive choices when faced with ambiguous or contradictory input. In this case, these models may produce uncertain or inconsistent responses, impacting the reliability and coherence of their output.
  • Implicit Knowledge: One weakness of large language models is their tendency to rely on implicit knowledge present in the training parameters, leading to biased or inaccurate outputs that reflect the biases and limitations of the data they were trained on.
  • Lacking Domain-Specific/New Knowledge: While large language models possess broad general knowledge, they may struggle with generalizing well to domain-specific or up-to-date information. They may provide outdated or incomplete responses in rapidly evolving fields, highlighting the challenge of keeping the models continuously updated with the latest knowledge.

Exploring Knowledge Graphs

Background

Knowledge graphs have emerged as robust tools for organizing and representing structured information in a machine-readable format. With roots in knowledge representation and graph theory, knowledge graphs provide a structured framework for capturing and connecting entities, their attributes, and their relationships.

Sample knowledge graph

These graphs leverage rich data connections to empower advanced reasoning, semantic search, and knowledge-based applications, paving the way for a deeper understanding and utilization of information in various domains.

Types of Knowledge Graphs

To capture different facets of knowledge and serve specific purposes, knowledge graphs can be classified into four categories, including:

  1. Common-Sense Knowledge Graphs focus on capturing everyday, intuitive knowledge about the world. They aim to model the implicit knowledge that humans possess, enabling machines to reason and make inferences based on this common-sense understanding. ConceptNet is an example of a common-sense knowledge graph.
  2. Domain-Specific Knowledge Graphs are tailored to specific domains or industries. They capture and organize structured information relevant to a particular field, such as healthcare or finance, enabling more specialized knowledge representation and reasoning. Wisecube’s Biomedical Knowledge Graph is an example of a domain-specific graph of biomedical information.
  3. Encyclopedic Knowledge Graphs capture and represent information from general encyclopedic sources. They cover a broad range of topics and provide structured representations of factual information, such as entities, their attributes, and relationships. Wikidata is a popular example of an encyclopedic graph that is collaboratively maintained by extracting information from Wikipedia.
  4. Multimodal Knowledge Graphs integrate information from multiple modalities, such as text, images, audio, and video. They capture a more comprehensive understanding of data by incorporating diverse sources of information. These graphs facilitate tasks like multimodal search, image-text matching, and recommendations. IMGpedia and Richpedia are examples of multimodal knowledge graphs that incorporate both text and image data.

Strength of Knowledge Graphs

Knowledge graphs possess a variety of strengths that make them valuable tools for various applications, some of which include:

  • Structural Knowledge Representation: Knowledge graphs provide a structured framework for representing interconnected knowledge. They enable efficient information organization, navigation, and querying, allowing users to explore and understand complex data interconnections.
  • Decisiveness: Knowledge graphs can aid in making decisive choices by providing explicit and well-defined relationships between entities. This enables machines and applications to reason and infer new knowledge based on the available information, supporting more informed decision-making processes.
  • Interpretability and Explainability: Knowledge graphs are designed to be human-interpretable and explainable. The explicit representation of entities and relationships allows for a transparent understanding of the data and the reasoning behind the connections, enhancing interpretability and making it easier to identify and address potential biases or errors.
  • Accuracy and Consistency: Knowledge graphs prioritize data quality and accuracy by incorporating validation mechanisms and data integration processes. This emphasis on accuracy helps maintain high reliability and consistency in the knowledge represented, which is crucial for decision-making and knowledge-based applications.
  • Domain-Specific Knowledge Capture: Knowledge graphs can be tailored to capture domain-specific information and relationships. This enables more focused and accurate analyses, insights, and applications in specialized areas, such as healthcare, finance, or scientific research.
  • Evolving Knowledge: Knowledge graphs can evolve and adapt to incorporate new information and updates. The graph can be expanded or modified as new data becomes available to represent the latest knowledge and ensure the information remains up-to-date.

Weaknesses of Knowledge Graphs

Despite their numerous strengths, there are some areas where the potential of knowledge graphs is limited. Here are some of the limitations of knowledge graphs:

  • Incompleteness: Knowledge graphs are limited by the information available during creation. They may not capture the entire knowledge in a domain and can have gaps or missing data. Incomplete knowledge representation can lead to limitations in understanding and decision-making when encountering unrepresented entities or relationships.
  • Unseen Facts and Updates: Knowledge graphs may not always reflect the most recent or unseen facts. New information or discoveries that are not yet incorporated into the graph may lead to potentially outdated knowledge. Maintaining and updating knowledge graphs to keep up with rapidly evolving domains can be a significant challenge.
  • Lacking Language Understanding: While knowledge graphs excel in capturing structured data, they may struggle with understanding natural language and unstructured text. Language understanding goes beyond the structured representation of knowledge and involves interpreting the nuances, context, and semantics conveyed through text, which can be challenging for knowledge graphs.

Unifying Large Language Models & Knowledge Graphs To Maximize Their Strengths & Address Their Weaknesses

Now that we understand the individual strengths and weaknesses of large language models and knowledge graphs, it becomes evident that a unified approach to combine their capabilities holds tremendous potential for addressing their respective limitations and enhancing their overall effectiveness.

Here are the different approaches to unifying large language models and knowledge graphs that can be leveraged to create more robust AI systems:

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Large Language Model-Augmented Knowledge Graphs

LLM-augmented Knowledge Graphs leverage the power of large language models to enhance the capabilities of knowledge graphs in various real-world applications. Traditional methods in knowledge graphs often struggle with handling incomplete information and processing text datasets for graph construction. To address this issue, large language models can be used in the following ways to augment knowledge graphs:

  • Information-rich representation: Large language models can be used as text encoders for knowledge graph-related tasks. These encoders can process the textual data within the graph to enrich its representation. 
  • Graph construction: Large language models can be employed to process original graph data entities and relations to facilitate knowledge graph construction. 

By integrating language models into knowledge graph workflows, LLM-augmented knowledge graphs offer a promising avenue for enhancing the reasoning capabilities and performance of knowledge graph-based applications.

Knowledge Graph-enhanced Large Language Models

Knowledge graphs store vast amounts of explicit and structured knowledge, offering an opportunity to enhance the knowledge awareness of large language models. Knowledge graphs can be used at various stages to enhance large language models, including:

  • Pre-training: One approach to KG-enhanced models involves incorporating a knowledge graph into the model during the pre-training stage, enabling the model to learn knowledge directly from the graph.
  • Inference: Another avenue of exploration is integrating the graph into the language model during the inference stage.
  • Interpretability: Knowledge graphs can be used to interpret large language models’ facts and reasoning processes, enhancing their interpretability.

The explicit information in knowledge graphs can help enhance large language models by overcoming hallucinations, improving interpretability, and accessing domain-specific knowledge, leading to more robust and effective language models.

Unified Large Language Models + Knowledge Graphs

Large language models and knowledge graphs are inherently complementary techniques that possess the potential to enhance each other mutually.

This unified framework comprises four layers:  

  1. Data layer: In this layer, large language models and knowledge graphs process textual and structural data, respectively.
  2. Synergized Model layer: In this layer, the model and graph collaborate to enhance their capabilities, leveraging their strengths and knowledge representations.
  3. Technique layer: This layer incorporates relevant techniques from both to further augment the performance of the synergized model.
  4. Application layer: In this layer, integrated large language models and knowledge graphs can be applied to various real-world applications, such as search engines, recommender systems, and AI assistants.

The unification of large language models and knowledge graphs can unlock the full potential of these techniques, enabling them to address complex challenges and drive advancements in various application domains.

Wisecube’s Unified Approach for Leveraging Large Language Models to Augment its Biomedical Knowledge Graph

The integration of large language models and knowledge graphs holds tremendous potential for advancing the field of natural language processing and knowledge representation. As these techniques evolve and mature, we can expect further breakthroughs in language understanding, knowledge discovery, and the emergence of context-aware AI systems.

At Wisecube, we are leveraging the full potential of this unification by utilizing large language models to process vast amounts of biomedical literature, extracting valuable insights, and enriching our biomedical knowledge graph with up-to-date medical knowledge. By synergizing with GPT-4, Wisecube is leveraging its robust AI technologies to reshape the biomedical landscape. Want to learn more about advancing your biomedical analytics? Get in touch with us today to explore the powerful capabilities of Wisecube’s Knowledge Graph Engine and GPT-4.

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