Knowledge Graph Completion for Rail Transit Maintenance Based on Directed Subgraphs and Dynamic Information Flow Tianjin University of Technology and School, Education of Au

Format:

Book

Conference/Event

Author/Creator:

Zhou, Lujie, author.

Contributor:

Gao, Sai

Liang, Chaohui

Zhang, Haifei

Conference Name:

2025 5th International Conference on Smart City Engineering and Public Transportation (SCEPT2025) (2025-03-28 : Beijing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

In order to deal with sparsity and incompleteness issues in the knowledge graph (KG) of urban rail transit operation and maintenance (O&M), this paper introduces a dynamic information flow based directed subgraph-based knowledge graph completion (KGC) method. Adding ontology constraints and semantic similarity calculations, the dynamic directed subgraph of new entities is constructed, enabling precise candidate entity and relation set selection, and successfully capturing contextually relevant domain information. Next, an embedding generation model with a dynamically updated information flow is constructed, integrating multi-layer message passing and self-attention mechanism to progressively obtain semantic features and structural dependencies from the subgraph and generate context-aware embeddings for entities and relations. Finally, the ConvE model acts as a decoder to learn higher-order entity and relation interactions in the triples and generate correct triple scores for efficient prediction and completion of missing relations. Experiments demonstrate that the proposed method outperforms state-of-the-art baselines on Mean Reciprocal Rank (MRR) and Hit Rates (Hits@1, Hits@3), confirming its effectiveness for KGC in urban rail transit O&M

Notes:

Vendor supplied data

Publisher Number:

2025-99-0049

Access Restriction:

Restricted for use by site license