Abstract
Link prediction is an effective method to guarantee the integrity of the knowledge graph, aiming to predict the missing part of the triple. So far most of the existing researches have been proposeed to embed entities and relations into a vector space or inferred the paths between entities in a knowledge graph. However, most of the previous works merely take account of the single path or first-order information, ignoring the relation between the entities and their attributes. Motivated by this, for a better representation of entities and relations, we in this article exploit the characteristics of the attribute to enrich the information of entities cooperated with a graph neural network. In our method the edges connected by a node are regarded as its contextual information, which will be extracted as an attribute feature. Then the message propagation network is utilized to generate the node and edge representions, after which an aggregation function is applied to integrate node attributes, node representation as well as edge representation to realize link prediction. Experiments on the same datasets show that our model outperforms the baselines in multiple metrics including MRR and Hits@N. At the same time, ablation experiments validate a strong expandability of the node attribute feature learning method we propose, which enables the model to accelerate the convergence of training and improve the performance on the link prediction task.
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The research is supported by The Natural Science Foundation of Guangdong Province (No. 2018A030313934).
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Zuo, Y., Zhou, Y., Yi, B., Zhan, M., Chen, K. (2022). Link Prediction via Fused Attribute Features Activation with Graph Convolutional Network. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13630. Springer, Cham. https://doi.org/10.1007/978-3-031-20865-2_8
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