Title: LLM-AUGMENTED KNOWLEDGE REPRESENTATION LEARNING VIA ATTENTION FOR KNOWLEDGE GRAPH COMPLETION
Primary area: learning on graphs and other geometries & topologies
Keywords: Knowledge Graph Completion, Large Language Models, Data Augmentation

Abstract: Knowledge Graph Completion (KGC) is a critical task, yet its performance is often hindered by the data sparsity problem arising from the long-tail distribution of entities. While existing works attempt to enrich representations by incorporating auxiliary information like entity descriptions, this kind of implicit learning approaches often proved ineffective due to the introduction of irrelevant noise. To address this, we propose a novel framework LAKRA, which shifts the paradigm from implicit knowledge encoding to explicit data augmentation. LAKRA leverages a Large Language Model (LLM) to proactively reason and generate high-quality, schema-compliant triples for sparse entities, mitigating data sparsity at its source. Besides, we design a powerful encoder-decoder architecture for representation learning, which features a query-aware hybrid attention encoder and a deep feature interaction decoder to capture complex structural and semantic patterns. Experiments conducted on the benchmark datasets demonstrate that LAKRA achieves highly competitive performance on link prediction tasks involving infrequent entities. Our work presents an effective new paradigm for tackling data sparsity in knowledge graphs.