| Summary: | Knowledge bases (KBs) have become an integral element in digitalization strategies for intelligent engineering and manufacturing. Existing KBs consist of entities and relations and deal with issues of newly added knowledge and completeness. To predict missing information, we introduce an expressive multi-layer network link prediction framework—namely, the convolutional adaptive network (CANet)—which facilitates adaptive feature recalibration by networks to improve the method’s representational power. In CANet, each entity and relation is encoded into a low-dimensional continuous embedding space, and an interaction operation is adopted to generate multiple specific embeddings. These embeddings are concatenated into input matrices, and an attention mechanism is integrated into the convolutional operation. Finally, we use a score function to measure the likelihood of candidate information and a cross-entropy loss function to speed up computation by reducing the convolution operations. Using five real-world KBs, the experimental results indicate that the proposed method achieves state-of-the-art performance.
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