In-Context Learning with Topological Information for Knowledge Graph Completion (2412.08742v1)

Abstract: Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answering, and decision-making. However, their effectiveness is often hindered by incompleteness, limiting their potential for real-world impact. While knowledge graph completion (KGC) has been extensively studied in the literature, recent advances in generative AI models, particularly LLMs, have introduced new opportunities for innovation. In-context learning has recently emerged as a promising approach for leveraging pretrained knowledge of LLMs across a range of natural language processing tasks and has been widely adopted in both academia and industry. However, how to utilize in-context learning for effective KGC remains relatively underexplored. We develop a novel method that incorporates topological information through in-context learning to enhance KGC performance. By integrating ontological knowledge and graph structure into the context of LLMs, our approach achieves strong performance in the transductive setting i.e., nodes in the test graph dataset are present in the training graph dataset. Furthermore, we apply our approach to KGC in the more challenging inductive setting, i.e., nodes in the training graph dataset and test graph dataset are disjoint, leveraging the ontology to infer useful information about missing nodes which serve as contextual cues for the LLM during inference. Our method demonstrates superior performance compared to baselines on the ILPC-small and ILPC-large datasets.

In-Context Learning with Topological Information for LLM-Based Knowledge Graph Completion

This paper introduces a method to enhance Knowledge Graph Completion (KGC) using LLMs, particularly focusing on utilizing in-context learning with topological information. Knowledge Graphs (KGs) are pivotal in representing structured information, serving applications like information retrieval and decision-making. However, KGs often suffer from incompleteness. Traditional KGC methods primarily focus on analyzing existing graph structures and utilizing machine learning techniques to address these gaps. The authors propose leveraging the capabilities of LLMs, which are known for handling natural language tasks effectively, even when the input data is vast and unstructured.

Core Methodology

The novel contribution of this research lies in its integration of graph topological information into LLM-based in-context learning processes to enhance both transductive and inductive KGC tasks:

1. Ontology Creation: The authors propose a generative approach to automatically create an ontology from raw KG data using LLMs. This ontology captures types of nodes (entities) and relationships, facilitating better contextual integration for LLMs during link prediction.
2. In-Context Learning Framework: The paper develops an in-context learning framework where the LLM is supplied with both ontology-informed context and the structural paths within the KG. This context allows the model to navigate and predict missing nodes more effectively.
3. Graph Topology Utilization: The approach incorporates topological features to unearth alternative paths between nodes, leveraging these as additional context for the LLM. This utilization is particularly useful for generating candidate solutions for missing node predictions, which the LLM refines based on the provided context.
4. Inductive and Transductive Settings: In the transductive setting, where inference nodes are present in the training data, the method enhances performance by focusing on node interconnections. In the inductive setting, where test nodes are disjoint from training data, the method leverages ontological categories and paths to facilitate node inference.

Experimental Insights

The authors validate their approach using datasets (ILPC-small and ILPC-large), showing impressive results:

• In the transductive setting, the method achieves notable Hit@k improvements over conventional LLM usage without topology context. Candidate solution generation significantly boosts model accuracy in predicting missing nodes.
• In the inductive setting, the combination of ontology hints and path contexts helps achieve state-of-the-art results, highlighting the method's robustness to missing data scenarios not covered in training.

Implications and Future Directions

This paper's insights underline the potential of combining LLMs with structured data elements from KGs to enhance machine reasoning capabilities. The primary implication is a more effective approach to bridging the gaps in large-scale KGs, which can improve applications like personalized recommendation systems and autonomous knowledge-based reasoning systems.

Future progress in this domain might involve:

• Developing more sophisticated node typing mechanisms within ontology creation to refine LLM predictions further.
• Exploring how continual learning paradigms can allow LLMs to update their understanding of KG structures dynamically.
• Extending experiments across diverse domains and KG datasets to understand the approach's adaptability and effectiveness across different contexts.

The paper represents a significant step in exploring the synergy between LLMs and relational data, aiming to create a more enriched framework for automated reasoning and knowledge extenuation in KGs.