Weight-aware tasks for evaluating knowledge graph embeddings

Wei-Kun Kong

doi:10.1016/j.knosys.2025.113596

Weight-aware tasks for evaluating knowledge graph embeddings

2025-06-01

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DOI：10.1016/j.knosys.2025.113596

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Knowledge graph embeddings encode knowledge by representing entities and relations through vectors or matrices and have been widely employed in conjunction with deep learning to address a diverse range of problems. The effectiveness of knowledge-driven tasks is intrinsically dependent on the quality of these embeddings. To enhance embedding quality, weight information has been incorporated to develop weight-aware knowledge graph embeddings. However, existing weight-aware knowledge graph embedding models are still evaluated using weight-agnostic tasks, indiscriminately treating all triples while disregarding the global weight distribution of the knowledge graph. To bridge this gap, we introduce weight-aware tasks specifically designed for knowledge graph embeddings, namely weight-aware link prediction and weight-aware triple classification, aiming to provide a more comprehensive evaluation of embedding models on weighted knowledge graphs. To validate the effectiveness of the proposed evaluation protocols, we present a general framework, WaExt, which extends conventional deterministic knowledge graph embedding models into their weight-aware counterparts. Extensive evaluations on four classical knowledge graph embedding models and three weighted knowledge graphs, it is demonstrated that the superiority of the proposed weight-aware evaluation protocol. Moreover, the WaExt framework WaExt achieves competitive performance, outperforming existing methods. The implementation is publicly available at: https://github.com/Diison/WaExt.

Keywords:

Knowledge graphs

Knowledge graph embedding

Evaluation protocol

Weight-aware tasks for evaluating knowledge graph embeddings

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