Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion

TL;DR

This paper introduces QASPR, a novel framework for inductive knowledge graph completion that adaptively filters noise and evaluates semantic contributions along reasoning paths, achieving state-of-the-art results.

Contribution

The paper proposes the QASPR framework, combining query-dependent masking and semantic scoring to improve inductive KGC, addressing noise and long-range dependency challenges.

Findings

QASPR outperforms existing methods on benchmark datasets.

The query-dependent masking effectively filters noisy structural information.

Semantic scoring enhances reasoning accuracy and interpretability.

Abstract

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.