A lightweight embedding method for knowledge graph quality evaluation

Knowledge graph (KG) quality evaluation seeks to assess the quality of triples in a KG. Existing approaches for KG quality evaluation often rely on high-dimensional embeddings to improve the model's evaluative performance. This reliance, however, not only increases the model's scale but also introduces feature redundancy, constraining its applicability to real-world problems. To tackle this challenge, this study proposes a Lightweight Embedding Method for KG Quality Evaluation (LEKGQE). This method performs kernel principal component analysis on each dimension of the triples, mapping multivariate data into univariate data, thereby effectively extracting the main features of the data. Furthermore, we quantify the contribution of each dimension on the target variable. Cross-entropy is used to identify the most discriminative features for KG quality evaluation, significantly reducing the model's embedding dimension and improving the KG quality evaluation performance under low-dimensional embeddings. Extensive experiments demonstrate the effectiveness of the LEKGQE model. With an embedding dimension of 32, the LEKGQE achieves a 14 % increase in F1 score on the FB15K dataset and a 12 % increase in F1 score on the WN18 dataset, markedly improving the model's performance in low-dimensional embedding scenarios.