UniLP: Unified Topology-aware Generative Framework for Link Prediction in Knowledge Graph

Ben Liu, Miao Peng, Wenjie Xu, Xu Jia, Min Peng

Abstract: Link prediction (LP) in knowledge graph (KG) is a crucial task that has received increasing attention recently. Due to the heterogeneous structures of KGs, various application scenarios, and demand-specific downstream objectives, there exist multiple subtasks in LP. Most studies only focus on designing a dedicated architecture for a specific subtask, which results in various complicated LP models. The isolated architectures and chaotic situations make it significant to construct a unified model that can handle multiple LP subtasks simultaneously. However, unifying all subtasks in LP presents numerous challenges, including unified input forms, task-specific context modeling, and topological information encoding. To address these challenges, we propose a topology-aware generative framework, namely UniLP, which utilizes a generative pre-trained language model to accomplish different LP subtasks universally. Specifically, we introduce a context demonstration template to convert task-specific context into a unified generative formulation. Based on the unified formulation, to address the limitation of transformer architecture that may overlook important structural signals in KGs, we design novel topology-aware soft prompts to deeply couple topology and text information in a contextualized manner. Extensive experiment results demonstrate that our framework achieves substantial performance gain and provides a real unified end-to-end solution for the whole LP subtasks. We also perform comprehensive ablation studies to support in-depth analysis of each component in UniLP.