Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning

SAT (Structure-Aware Alignment-Tuning) is a unified framework that bridges the representational gap between graph structures and natural language in LLMs to improve knowledge graph completion tasks. It uses hierarchical knowledge alignment and structural instruction tuning to enable LLMs to perform structure-aware reasoning over knowledge graphs.

Problem Statement

LLM-based knowledge graph completion methods suffer from a fundamental mismatch between the continuous vector spaces of graph embeddings and natural language representations, limiting effective knowledge transfer. Additionally, existing approaches require designing separate task-specific instructions for each KGC task, leading to redundant engineering effort and inefficiency. These limitations prevent LLMs from fully leveraging structural information inherent in knowledge graphs during reasoning.

Key Novelty

Hierarchical knowledge alignment that uses multi-task contrastive learning to explicitly align graph embedding spaces with natural language representation spaces
Structural instruction tuning with a unified graph instruction template that handles multiple KGC tasks simultaneously, eliminating the need for task-specific instruction design
A lightweight knowledge adapter that injects structure-aware graph information into the LLM without requiring full fine-tuning, enabling efficient integration of graph structure into LLM reasoning

Evaluation Highlights

Significant improvements in link prediction task ranging from 8.7% to 29.8% over state-of-the-art methods across four benchmark datasets
Demonstrated superiority on two distinct KGC tasks (including link prediction and likely triple classification) across four benchmark datasets, showing broad generalizability of the unified instruction approach

Breakthrough Assessment

6/10 SAT makes a solid contribution by addressing a well-recognized but underexplored problem (representation space misalignment) with a principled solution combining contrastive alignment and unified instruction tuning, yielding strong empirical gains. However, it remains an incremental advance within the established LLM-for-KGC paradigm rather than a fundamental shift in how graphs and language are jointly modeled.

Methodology

Step 1 - Hierarchical Knowledge Alignment: Apply multi-task contrastive learning to align pre-trained graph structure embeddings into the natural language embedding space, ensuring structural information is interpretable by the LLM
Step 2 - Unified Graph Instruction Construction: Design a single unified instruction template that encodes both graph structure context and task requirements, replacing multiple task-specific instructions and reducing engineering overhead
Step 3 - Structural Instruction Tuning with Knowledge Adapter: Fine-tune the LLM using the unified graph instructions while injecting aligned graph embeddings through a lightweight adapter module, enabling structure-aware reasoning during inference

System Components

Hierarchical Knowledge Alignment

A multi-task contrastive learning module that maps graph entity/relation embeddings from graph space into the LLM's natural language representation space, reducing the modality gap

Unified Graph Instruction

A single instruction template that encodes structural context (neighborhood information, relational paths) and supports multiple KGC tasks, replacing task-specific prompt engineering

Lightweight Knowledge Adapter

A parameter-efficient adapter module inserted into the LLM that injects aligned graph structural embeddings during forward passes, enabling structure-aware reasoning without full model retraining

Structure-Aware Reasoning Pipeline

The end-to-end inference pipeline that combines the aligned graph representations with LLM reasoning capabilities to perform KGC predictions

Results

Task/Benchmark	Best Baseline (SOTA)	SAT	Delta
Link Prediction (best dataset)	SOTA baseline	SAT result	+29.8% (max improvement)
Link Prediction (worst dataset)	SOTA baseline	SAT result	+8.7% (min improvement)
KGC Tasks (avg across 4 datasets)	Previous SOTA	SAT	+8.7% to +29.8% range

Key Takeaways

Explicitly aligning graph embedding spaces with language model spaces via contrastive learning is a critical but often overlooked step when integrating KG structural information into LLMs — practitioners should treat this as a necessary preprocessing stage rather than assuming the spaces are compatible
Designing unified instruction templates across multiple graph tasks not only reduces engineering effort but can also improve model performance by enabling shared learning across related KGC tasks during fine-tuning
Lightweight adapter modules offer a practical and compute-efficient mechanism to inject structured, non-textual information (e.g., graph embeddings) into frozen or lightly fine-tuned LLMs, making this approach feasible for production deployments with limited GPU resources

Abstract

Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning capabilities. LLM-enhanced KGC methods primarily focus on designing task-specific instructions, achieving promising advancements. However, there are still two critical challenges. First, existing methods often ignore the inconsistent representation spaces between natural language and graph structures. Second, most approaches design separate instructions for different KGC tasks, leading to duplicate works and time-consuming processes. To address these challenges, we propose SAT, a novel framework that enhances LLMs for KGC via structure-aware alignment-tuning. Specifically, we first introduce hierarchical knowledge alignment to align graph embeddings with the natural language space through multi-task contrastive learning. Then, we propose structural instruction tuning to guide LLMs in performing structure-aware reasoning over KGs, using a unified graph instruction combined with a lightweight knowledge adapter. Experimental results on two KGC tasks across four benchmark datasets demonstrate that SAT significantly outperforms state-of-the-art methods, especially in the link prediction task with improvements ranging from 8.7% to 29.8%.