A Pre-trained Universal Knowledge Graph Reasoning Model Based on Rule Prompts

A knowledge graph (KG) is a structured knowledge base that stores a massive amount of real-world knowledge, providing data support for numerous knowledge-driven downstream tasks. KGs often suffer from incompleteness, with many missing facts. Therefore, the KG reasoning task aims to infer new conclusions based on known facts to complete the KG. With the research and development of knowledge engineering and its commercial applications, numerous general and domain-specific KGs have been constructed. Existing KG reasoning models mostly focus on completing a single KG but lack general reasoning capabilities. Inspired by the general capabilities of pre-trained large language models in recent years, some pre-trained universal KG reasoning models have been proposed. Addressing the issue of existing pre-trained model being unable to identify high-quality reasoning patterns, we introduce a rule-based pre-trained universal KG reasoning model called RulePreM. It discovers and filters high-quality reasoning rules to enhance the reasoning abilities. The proposed model first constructs a relational IO graph based on reasoning rules and uses an encoder, RuleGNN, to encode the relations. The encoded relations are then used as prompts to encode entities in the KG. Finally, candidate entities are scored for prediction. Additionally, an attention mechanism that combines rule confidence is introduced to further reduce the impact of low-quality reasoning patterns. Experimental results demonstrate that the proposed model exhibits universal reasoning abilities on 43 different KGs, with average performance surpassing existing supervised and pre-trained models.