2024-02-26: Paper Summary: Unifying Large Language Models and Knowledge Graphs: A Roadmap

Comparison of the pros and cons of a Large Language Model and a Knowledge Graph and how they compliment each other when integrated (Pan et al.)

In my previous blog post, ALICE - AI Leveraged Information Capture and Exploration,  I propose a system for archiving and storing routinely lost data using presentation slide decks as the primary use-case. This project serves as an exploratory test environment for merging structured data models to Large Language Models (LLMS) for research into injecting uncertainty quantification and auditing for hallucinations in LLMS. In "Unifying Large Language Models and Knowledge Graphs: A Roadmap" by Shirui Pan et al., published in the 2024 IEEE Transactions on Knowledge and Data Engineering (TKDE) journal, the authors present a notional framework to explore the integration of LLMs like GPT-4 with Knowledge Graphs (KGs). In the following sections, I will step through the authors' analysis and main contributions. We'll explore how the authors dissect the capabilities and limitations of both LLMs and KGs, delving into their distinct characteristics and the challenges they face. The paper identifies the emerging abilities of LLMs in AI and their generalization across various tasks and also the limitations of LLMs in accessing factual knowledge. KGs, in contrast, offer structured and accurate knowledge representation but face challenges in construction and dynamic updating. We will then explore the authors' main contribution — three integrative frameworks—KG-Enhanced LLMs, LLM-Augmented KGs, and Synergized LLMs + KGs, revealing how they could potentially revolutionize the AI field by merging the strengths of both LLMs and KGs to mitigate their respective weaknesses. We will also examine the authors' survey of current research efforts in this area and identify the knowledge gaps that they highlight for future exploration. This step-by-step walkthrough aims to provide a comprehensive understanding of the paper's contributions to the evolving landscape of artificial intelligence.

LLMs

Representative large language models (LLMs) in recent years. Open-source models are represented by solid squares, while closed source models are represented by hollow squares. (Pan et al.)

The development of LLMs has been significantly advanced by modern deep-learning techniques, particularly the use of Transformer models. Transformers, introduced in "Attention Is All You Need" by Vaswani et al., represent a shift in how models process sequential data. Unlike previous methods that processed data in order, Transformers allow for parallel processing, greatly enhancing efficiency and scalability. This architecture uses self-attention mechanisms, enabling the model to weigh the importance of different parts of the input data. As a result, LLMs trained with Transformer architectures can better understand context and generate more coherent and contextually relevant text. This approach has been pivotal in the evolution of LLMs, allowing them to handle complex language tasks with greater accuracy. LLMs have demonstrated remarkable performance in various natural language processing tasks. They are mainly categorized based on their architectural design into three types: encoder-only, encoder-decoder, and decoder-only models.

The general architecture of a Transformer model (Pan et al.)

1. Encoder-only LLMs: These models, like BERT and RoBERTa, focus on encoding input text and understanding word relationships. They are particularly adept at tasks requiring comprehensive sentence understanding, such as text classification and entity recognition.
2. Encoder-decoder LLMs: Models like T5 and GLM-130B fall under this category. They use both encoder and decoder modules for tasks like text summarization, translation, and question answering. These models encode the input and then generate relevant output text.
3. Decoder-only LLMs: GPT-3 and ChatGPT are examples of this type, which use only a decoder to generate text. These models excel in generating coherent and contextually relevant text and are often used for tasks that require fewer examples or simple instructions.

Pan (2024) illustrates these types with various examples and discusses their specific applications and strengths. This overview of LLMs provides a foundational understanding of their structure and functionality in the field of natural language processing.

KGs

Visualization of a knowledge graph (Youseth Seth)

1. Entities as Nodes: In a knowledge graph, each node represents an entity. An entity could be anything with a distinct and independent existence, like a person, a company, a city, a movie, a scientific concept, etc.
2. Relationships as Edges: The edges in a knowledge graph represent the relationships between entities. For example, in a knowledge graph about people, an edge might represent a relationship like "works for,""married to," or "born in."
3. Rich Data Representation: Knowledge graphs are capable of representing complex interconnections in data. They go beyond simple data storage by linking related pieces of information, thereby creating a network of knowledge that can be used for various applications like semantic search, recommendation systems, or AI reasoning.
4. Semantic Information: Knowledge graphs often include semantic information, meaning they can incorporate the meanings and contexts of entities and their relationships. This allows for more intelligent data retrieval and analysis, as the graph understands the nature of the connections it represents.
5. Dynamic and Scalable: Knowledge graphs can be dynamically updated with new information and are scalable. As new entities and relationships are discovered, they can be added to the graph, continuously enriching the dataset.
6. Use in AI and Machine Learning: In AI, knowledge graphs are used to enhance machine learning models, providing them with context and background knowledge. This can improve the accuracy of tasks like natural language processing, data analysis, and decision-making processes.

Overall, knowledge graphs are a powerful tool for organizing and leveraging data in a way that mimics human understanding and reasoning, making them invaluable in various fields, from web search engines to advanced research.

The challenges in constructing and maintaining KGs are significant, as highlighted by recent research in this area such as Kumar (2020) and Melnyk (2021). KGs are known for their ability to store structured factual knowledge, but the process of constructing and updating them is complex and multi-faceted. One of the main challenges in constructing KGs is the need for a powerful graph data model that can support entities and relations of different types, along with their ontological description and organization. This model should also provide a comprehensive query language and capabilities for advanced graph analysis or mining. Ensuring data integrity and quality is crucial, which requires support for integrity constraints to control the consistency of the graph data. Additionally, it's important to represent annotating metadata for KG entities, such as information about their origin and transformation during KG construction. Knowledge graphs typically integrate information from multiple sources into a graph-like representation, which makes them schema-flexible and capable of accommodating heterogeneously structured entities. This flexibility, however, also introduces complexity, especially when compared to more static data integration methods like data warehouses. KGs need to be able to handle semi- and unstructured data from various sources, which poses its own set of challenges. Moreover, the temporal aspect of knowledge graphs is important; they should be capable of reflecting changes over time. This requires a temporal graph data model with time metadata for every entity and relation, allowing for temporal analysis such as determining previous states of the KG or changes over a certain time interval.

The two most popular graph data models used for KGs are the Resource Description Framework (RDF) and the Property Graph Model (PGM). Each of these models has its own set of features and limitations. RDF, for example, is a data exchange format initially developed for describing web-resources metadata and is widely used for building and using KGs. PGM, on the other hand, is a more flexible model supporting heterogeneous nodes and directed edges, with both nodes and edges capable of having multiple labels and properties.

Despite these capabilities, both models present challenges in terms of data integrity, query language complexity, and the representation and querying of temporal information. Addressing these challenges is crucial for the effective construction and continuous maintenance of KGs. The research in this field is ongoing, with continuous efforts to refine the models, address their limitations, and enhance their capabilities to make KG construction and maintenance more efficient and effective.

Roadmap & Categorization:

The general roadmap of unifying KGs and LLMs (Pan et al.)

Proposed Approaches

KG-enhanced LLMs

Integrating KGs into Training Objective: The paper suggests modifying the LLMs' loss functions to incorporate knowledge-aware components, encouraging the model to align its predictions with facts stored in KGs. This could involve tasks like entity prediction, relation prediction, or knowledge embedding alignment. 

 Integrating KGs into LLM Inputs: For this strategy, KG information is preprocessed and embedded into the input data of the LLMs. Techniques like entity embedding, relation embedding, and graph neural networks could be used to transform the structured KG information into a format that is compatible with the LLMs' input representations. 

Integrating KGs by Additional Fusion Modules: The approach includes developing specialized neural network modules that are capable of merging the representations from the LLM and KGs. These modules can be designed to dynamically select and integrate relevant knowledge from KGs based on the context of the input text, improving the LLM's ability to understand and generate knowledge-rich content.

KG-enhanced LLM inference: KG-enhanced LLM inference aims to bridge the gap between the generative capabilities of LLMs and the structured knowledge contained in KGs. The integration of KGs into LLM inference involves techniques such as dynamic knowledge fusion and retrieval-augmented knowledge fusion. Dynamic knowledge fusion allows LLMs to adaptively incorporate relevant KG information during the inference process, enabling them to access and utilize up-to-date and specific knowledge. Retrieval-augmented knowledge fusion, on the other hand, involves querying a knowledge base (often in the form of a KG) in real-time to retrieve relevant information that can be incorporated into the model's outputs. 

Retrieval-Augmented Knowledge Fusion: This method involves dynamically integrating information retrieved from a KG during the inference process. It aims to enhance the factual accuracy of the LLM's outputs by providing access to up-to-date and specific knowledge contained within the KG. Techniques include querying the KG based on the context of the input text and fusing the retrieved information with the generative capabilities of the LLM to produce enriched responses.

Retrieving external knowledge to enhance the LLM generation (Pan et al.)

KGs Prompting: KGs Prompting involves structuring inputs to the LLM in a way that mimics queries to a KG, leveraging the structured nature of KGs to elicit more accurate and relevant responses. This method capitalizes on the inherent structure of KGs (entities, relations, attributes) to formulate prompts that guide the LLM towards generating responses that are aligned with the factual knowledge represented in the KG. It can be used to directly incorporate KG information into the model's prompts, thus enhancing the model's ability to generate responses based on the knowledge encapsulated in the KG.

KG-enhanced LLM Interpretability: The integration of KGs into the interpretability analysis of LLMs represents a groundbreaking approach to demystifying LLMs. By probing and analyzing LLMs through the structured lens of KGs, researchers can gain unprecedented insights into the models' knowledge base and reasoning processes. This not only enhances our understanding of LLMs but also paves the way for more transparent, reliable, and interpretable AI systems in high-stakes applications across various domains. There are two main approaches used for LLM interpretability:

Probing LLMs with KGs: The technique of probing aims to unveil the vast, yet hidden, knowledge stored within LLMs. Given the models' propensity to sometimes produce factually incorrect "hallucinations," verifying the knowledge they contain becomes paramount. One pioneering approach in this context is the LAMA (LAnguage Model Analysis) framework, which employs KGs to convert facts into cloze statements. These statements then serve as prompts for LLMs, testing their ability to predict missing entities accurately. This method provides a direct evaluation of the knowledge LLMs have internalized. However, not all prompts are created equal. Some may be more conducive to accurate predictions than others. Recognizing this, subsequent approaches like LPAQA and Autoprompt have refined the process, generating high-quality, diverse prompts that offer a more precise assessment of the LLMs' knowledge. Furthermore, specialized probes such as BioLAMA and MedLAMA focus on domain-specific knowledge, highlighting the models' proficiency in areas like medicine.

The general framework of using knowledge graph for language model probing (Pan et al.)

Analyzing LLMs through KGs: Beyond probing, KGs play a pivotal role in analyzing the inner workings of LLMs. Questions like "How do LLMs generate results?" and "What underpins their reasoning process?" are at the forefront of this inquiry. Tools such as KagNet and QA-GNN ground the LLM-generated outputs in KGs, enabling a step-by-step explanation of the reasoning process based on the extracted KG structure. This analysis extends to understanding the reliance on word patterns, with studies indicating that LLMs may favor positionally closer words over those that are more knowledge-dependent. This suggests a potential limitation in the models' ability to accurately memorize and utilize factual knowledge. Moreover, the concept of "knowledge neurons" introduced by Dai et al. marks a significant leap towards decoding how knowledge is represented within LLM parameters. By identifying and manipulating these neurons, researchers can explore the specific knowledge and facts each neuron represents, providing deeper insights into the models' knowledge encoding mechanisms.

The general framework of using knowledge graph for language model analysis (Pan et al.)

LLM-augmented KGs

LLMs as Text Encoders: This sub-domain explores using LLMs to encode text information within knowledge graphs. The idea is to leverage the natural language understanding capabilities of LLMs to improve the representation of text data in KGs.

LLMs as text encoder for knowledge graph embedding (KGE) (Pan et al.)

LLMs for Joint Text and KG Embedding: This area focuses on developing methods where LLMs are employed to jointly embed both text data and structured knowledge graph data. This approach aims to create embeddings that capture the nuanced interplay between unstructured text and structured KG information, enhancing the overall quality and utility of the embeddings for various tasks.

LLMs for joint text and knowledge graph embedding (Pan et al.)

LLM as Encoders (PaE): Explores how LLMs can serve as encoders to enhance KGs by embedding text data into them. LLMs can effectively transform text data into a format that can be seamlessly integrated with KGs, enhancing their depth and accuracy. The focus here is on the capacity of LLMs to comprehend and process natural language, which can be leveraged to enrich the knowledge graph's entities and relationships, thereby making them more meaningful and contextually relevant.

The general framework of adopting LLMs as encoders (PaE) for KG Completion (Pan et al.)

 

LLM as Generators (PaG): Focuses on the use of LLMs to generate new knowledge or entities for KGs. This approach involves using LLMs to generate new entities and relationships to fill gaps in KGs. It emphasizes leveraging the advanced natural language generation capabilities of LLMs to create contextually relevant and coherent new knowledge, thereby enhancing the richness and completeness of the KGs.

The general framework of adopting LLMs as decoders (PaG) for KG Completion. The En. and De. denote the encoder and decoder, respectively (Pan et al.)

LLM-augmented KG construction: LLM-augmented KG construction involves using LLMs to enhance the creation and enrichment of knowledge graphs. 

The general framework of LLM-based KG construction (Pan et al.)

This process integrates several key tasks:

Entity Discovery: Identifying and extracting entities from unstructured text. LLMs can be used to detect potential entities that are not already present in the knowledge graph. Entity Discovery is a multi-part process to extract various entities from the text in order to inject them into a KG. The steps involved are Named Entity Recognition, Entity Typing and Entity Linking:

Named Entity Recogntion (NER): Identifies and extracts named entities from unstructured text. This is the foundational step in KG construction, where entities like people, organizations, and locations are recognized and tagged with their respective categories.  

Entity Typing (ET): Assigns fine-grained type information to entities identified in textual data. This task is crucial for enhancing the semantic richness of KGs, enabling a more nuanced understanding of the entities and their relationships. It provides detailed type classifications for entities mentioned within a context, going beyond basic categorizations to include nuanced and specific types. An example given in the paper is LDET, which uses pre-trained ELMo embeddings for word representation and LSTM for encoding sentences and mentions. This method illustrates how ET combines advanced language models and neural network architectures to improve the specificity and accuracy of entity type information within KG construction​.

Entity Linking (EL): Also known as entity disambiguation, involves mapping entity mentions within text to their corresponding entities in a KG. The paper describes several advanced approaches to EL, emphasizing the use of BERT-based systems and novel architectures to improve the accuracy and efficiency of linking entities. 

Coreference Resolution (CR): Aims to identify and link all expressions that refer to the same real-world entity or event within text. This task is instrumental in parsing and understanding narrative threads, ensuring that subsequent analysis, like knowledge graph construction or question answering, is based on a comprehensive understanding of the text's entities and their interrelations. There are two levels of CR needed for KG construction:

Within-document CR focuses on resolving references within the confines of a single document, where all entity mentions pertain to the same narrative or discourse context.

Cross-document CR extends the scope of CR to encompass multiple documents, linking mentions that refer to identical entities or events scattered across different texts.

Relation Extraction (RE): Aims to discern and classify semantic relationships between entities mentioned across texts, providing a structured understanding that is crucial for numerous applications, including knowledge graph completion, information retrieval, and intelligent question answering systems. This task also consists of two levels of processing:

End-to-End KG Construction: Aims to directly generate structured knowledge from unstructured text, embodying a unified method that significantly enhances efficiency and effectiveness in knowledge representation and extraction. Researchers are delving into the utilization of LLMs for a holistic approach to KG construction, integrating entity recognition, relation extraction, and knowledge distillation within a singular workflow. Notable methodologies and advancements include:

Distilling Knowledge Graphs from LLMs: The distillation of explicit knowledge from the implicit, vast knowledge reservoirs contained within LLMs. Approaches such as COMET leverage LLMs to generate high-quality commonsense knowledge tuples, transforming the tacit knowledge within LLMs into explicit, structured representations suitable for KG integration.

The general framework of distilling KGs from LLMs (Pan et al.)

 This LLM-augmented approach to KG construction can significantly enhance the scalability, richness, and accuracy of knowledge graphs, making them more dynamic and reflective of the vast amount of information encoded in natural language texts.

KG-to-text generation: The transformation of structured knowledge from KGs into coherent and contextually rich text to address the challenge of bridging structured and unstructured data

The general framework of KG-to-text generation (Pan et al.)

Leveraging Knowledge from LLMs

Integration of LLM Insights: Incorporates the vast and nuanced understanding of language and knowledge embedded within LLMs into the KG-to-text generation process. By tapping into the LLMs' pre-trained knowledge base, it's possible to enrich the text generation with deeper context, more varied language use, and improved accuracy in reflecting the underlying data of the KG.

Semantic Enrichment: LLMs help in semantically enriching the KG entities and relations by providing contextual insights. This enrichment enables the generation of text that is not only accurate but also contextually rich and nuanced, making the output more informative and engaging.

Ambiguity Resolution: Leveraging LLMs aids in resolving ambiguities inherent in translating structured data into natural language, ensuring the generated text is clear and unambiguous.

Constructing Large Weakly KG-Text Aligned Corpus

Corpus Creation: This involves compiling a vast dataset that pairs segments of text with corresponding elements and structures within a KG, albeit in a "weakly" aligned manner. Weak alignment means that the connections between text and KG elements are not meticulously mapped one-to-one but are instead loosely associated, allowing for flexibility in the text generation process.

Utilizing Weak Alignment: The weak alignment is strategically used to train models on a broader range of data, helping the system learn to generate text from KGs even when precise mappings between KG elements and text are not strictly defined. This approach enables the model to handle a wider variety of KG structures and content, making it more versatile.

Scalability and Adaptability: The creation of a large, weakly aligned corpus facilitates scalability in training, allowing the model to adapt to different domains or types of KGs. This adaptability is crucial for applications across various fields, from academic research to industry-specific knowledge bases.

By combining the deep, contextual knowledge of LLMs with the structured information in KGs, and leveraging a large, weakly aligned corpus, this approach significantly advances the state of KG-to-text generation. It promises more accurate, context-aware, and versatile text generation capabilities, which can enhance a wide array of applications, from automated reporting and content creation to more interactive and informative conversational agents.

KG question answering (KGQA): Leverages the structured world knowledge encapsulated within KGs to provide precise answers to natural language queries, a task that demands intricate understanding and reasoning over complex relationships and entities. Recent advancements have seen LLMs playing a pivotal role in enhancing KGQA systems, bridging the gap between the natural language of queries and the structured data within KGs. Here’s an overview of how LLMs contribute to KGQA:

The general framework of applying LLMs for KGQA (Pan et al.)

LLMs as Entity/Relation Extractors: LLMs are utilized to identify and extract entities and their relations from text data. This capability is fundamental for enriching KGs with new information or updating existing knowledge. The paper likely explores methodologies, challenges, and advancements in employing LLMs for this task, potentially covering aspects like the precision of extraction, handling of ambiguous entities, and integration with existing KG frameworks. 

LLMs as Answer Reasoners: In this role, LLMs leverage the structured information within KGs to provide reasoned answers to complex queries. This process involves encoding the query alongside the relevant KG facts and employing dynamic reasoning mechanisms to infer the most plausible answers. Notably, frameworks like DEKCOR and DRLK concatenate retrieved facts with queries and utilize LLMs to score candidate answers, showcasing LLMs' capability to contextualize and reason over structured knowledge.

Synergized LLMs + KGs: The integration of LLMs and KGs marks a pivotal advancement in the field of artificial intelligence, fostering a synergy that enhances performance across a variety of applications. This fusion leverages the natural language understanding capabilities of LLMs with the structured, factual knowledge provided by KGs, leading to significant improvements in knowledge representation and reasoning tasks. Here, the authors explore the synergized approach of LLMs and KGs from two critical perspectives:

Synergized Knowledge Representation:Text corpora contain vast amounts of knowledge in an implicit and unstructured format, whereas KGs offer explicit and structured knowledge. Bridging the gap between these two knowledge forms is essential for creating a unified representation.

Synergized knowledge representation by additional KG fusion modules (Pan et al.)

The authors describe several current solutions: 

KEPLER introduces a unified model for knowledge embedding and pre-trained language representation, encoding textual entity descriptions with LLMs and optimizing both knowledge embedding and language modeling objectives.

JointGT proposes a graph-text joint representation learning model, aligning representations of graph and text through three pre-training tasks.

DRAGON offers a self-supervised method to pre-train a joint language-knowledge foundation model, fusing information from text segments and relevant KG subgraphs, and optimizing the model with tasks like masked language modeling and KG link prediction.

TravelBERT introduces a unified LLM that incorporates KGs to learn domain-specific knowledge representations, showcasing the potential of integrating structured KG information with the linguistic capabilities of LLMs.

Synergized Reasoning: To leverage the combined strengths of LLMs and KGs for reasoning across various applications, enhancing the interpretability and effectiveness of the reasoning process.

The framework of LLM-KG Fusion Reasoning (Pan et al.)

 The authors review several current state-of-the-art approaches for synerigized reasoning:

QA-GNN utilizes LLMs to process text questions and guide reasoning on KGs, bridging the gap between textual and structural information.

LARK introduces a LLM-guided logical reasoning method, transforming conventional logical rules into language sequences for LLMs to reason over.

Wang et al. present a unified framework that combines structure reasoning and language model pre-training, generating logical queries executed on KGs to obtain structural context, which is then fused with textual information for final output generation.

This synergistic approach not only marries the deep linguistic understanding of LLMs with the factual, structured knowledge contained in KGs but also paves the way for innovative models capable of complex knowledge representation and reasoning. As this field continues to evolve, the potent combination of LLMs and KGs is set to redefine the boundaries of what is possible in artificial intelligence, offering more robust, interpretable, and effective solutions across a broad spectrum of applications.

Future Directions and Milestones

Future Directions

KGs for Hallucination Detection in LLMs: LLMs sometimes generate factually incorrect content, known as hallucinations, which undermine their reliability. Utilizing KGs as an external source to validate the content generated by LLMs and developing a generalized fact-checking model that integrates LLMs and KGs for cross-domain hallucination detection.

KGs for Editing Knowledge in LLMs: LLMs struggle to quickly update their stored knowledge to reflect real-world changes. Proposals for editing knowledge within LLMs without retraining the entire model, though current solutions are limited and face issues like catastrophic forgetting and incorrect knowledge editing.

KGs for Black-box LLMs Knowledge Injection: Many state-of-the-art LLMs provide only API access, making them black boxes that are challenging to update with the latest knowledge through conventional KG injection methods. Developing effective strategies for knowledge injection into black-box LLMs, possibly through converting knowledge into text prompts, while addressing limitations such as prompt generalizability and input length restrictions.

Multi-Modal LLMs for KGs: Real-world KGs often contain data from diverse modalities, not just textual or graph structures. Developing methods for accurately encoding and aligning entities across different modalities, with multi-modal LLMs offering promising avenues for modality alignment.

Synergized LLMs and KGs for Bidirectional Reasoning: Achieving a synergistic integration of LLMs and KGs to enhance both generative and reasoning capabilities, overcoming the individual limitations of each technology. Incorporating advanced techniques such as multi-modal learning, graph neural networks, and continuous learning. The synergized approach can be applied to a wide range of real-world applications, including search engines, recommender systems, and drug discovery, offering efficient and effective solutions.

Milestones

Unified Knowledge Representation: Achieving a seamless integration of the implicit, unstructured knowledge of LLMs with the explicit, structured information of KGs to create a comprehensive knowledge base accessible through natural language queries.

Advanced Reasoning Capabilities: Leveraging the combined strengths of LLMs and KGs to perform advanced reasoning over complex queries, surpassing the current limitations of each model when used in isolation.

Conclusion

Sources