Prompt-fused framework for Inductive Logical Query Answering
Zezhong Xu Peng Ye Lei Liang Huajun Chen Wen Zhang
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.