  Conventional Knowledge graph completion (KGC) methods aim to infer missing
information in incomplete Knowledge Graphs (KGs) by leveraging existing
information, which struggle to perform effectively in scenarios involving
emerging entities. Inductive KGC methods can handle the emerging entities and
relations in KGs, offering greater dynamic adaptability. While existing
inductive KGC methods have achieved some success, they also face challenges,
such as susceptibility to noisy structural information during reasoning and
difficulty in capturing long-range dependencies in reasoning paths. To address
these challenges, this paper proposes the Query-Enhanced Adaptive Semantic Path
Reasoning (QASPR) framework, which simultaneously captures both the structural
and semantic information of KGs to enhance the inductive KGC task.
Specifically, the proposed QASPR employs a query-dependent masking module to
adaptively mask noisy structural information while retaining important
information closely related to the targets. Additionally, QASPR introduces a
global semantic scoring module that evaluates both the individual contributions
and the collective impact of nodes along the reasoning path within KGs. The
experimental results demonstrate that QASPR achieves state-of-the-art
performance.
