Weight-aware tasks for evaluating knowledge graph embeddings

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’s 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 .