Prompt-fused framework for Inductive Logical Query Answering
This work addresses the problem of inductive logical query answering for knowledge graph applications, representing an incremental improvement by integrating and enhancing existing methods.
The authors tackled the challenge of answering logical queries on knowledge graphs, particularly addressing the incompleteness due to new entities and disjointed reasoning over logical operators, by proposing a prompt-fused framework that incorporates existing methods and introduces query prompts, achieving successful handling of unseen entities as demonstrated in experiments.
Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of KGs. Existing research has predominantly focused on addressing the issue of missing edges in KGs, thereby neglecting another aspect of incompleteness: the emergence of new entities. Furthermore, most of the existing methods tend to reason over each logical operator separately, rather than comprehensively analyzing the query as a whole during the reasoning process. In this paper, we propose a query-aware prompt-fused framework named Pro-QE, which could incorporate existing query embedding methods and address the embedding of emerging entities through contextual information aggregation. Additionally, a query prompt, which is generated by encoding the symbolic query, is introduced to gather information relevant to the query from a holistic perspective. To evaluate the efficacy of our model in the inductive setting, we introduce two new challenging benchmarks. Experimental results demonstrate that our model successfully handles the issue of unseen entities in logical queries. Furthermore, the ablation study confirms the efficacy of the aggregator and prompt components.