research-article

Feature enhancement based on hierarchical reconstruction framework for inductive prediction on sparse graphs

Authors: Xiquan Zhang, Jianwu Dang, Yangping Wang, Shuyang LiAuthors Info & Claims

Volume 62, Issue 1

https://doi.org/10.1016/j.ipm.2024.103894

Published: 20 February 2025 Publication History

Abstract

Knowledge graph completion aims to infer the missing links of new elements, however, the missing links often lie in sparse regions of the graph. Primary subgraph-based methods rely heavily on structural information, which makes it difficult for them to play an essential role in sparse graph completion. To address this challenge, we propose a learning framework for feature-enhanced hierarchical reconstruction (FEHR). The proposed FEHR explores relational semantics at the global and local levels, minimizing the limitations of sparse structures. First, entity graphs are converted into relation graphs, and overreliance on the entity structure is reduced by obtaining prior knowledge on similar global graphs. Second, the relational features are further refined at the local level. Finally, an improved performer model expresses the degree of preference between the predicted behaviors and relations. Extensive inductive experiments showed that FEHR performs better than state-of-the-art baselines, achieving improvements in area under the prediction–recall curve (AUC-PR) and Hits@n metrics, ranging from 0.32% to 11.73%.

Highlights

•

We construct a novel hierarchical reconstructed feature learning framework.

•

Global acquisition of additional prior knowledge through similarity graphs.

•

Locally distinguish the degree of association of relationships.

•

FEHR alleviates the limitation of a few links in sparse graphs.

•

Our model outperforms advanced baselines on most benchmark datasets.

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Information & Contributors

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Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 62, Issue 1

Jan 2025

1582 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 20 February 2025

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