Inductive Link Prediction for Sequential-emerging Knowledge Graph

Inductive Link Prediction (ILP) aims to predict links for unseen entities in emerging Knowledge Graphs (KGs), where a more realistic scenario is that unseen entities do not emerge all at once but emerge sequentially in multiple stages. Unfortunately, existing studies neglect the sequential-emerging nature of KGs and simplify this scenario into multi-batch unseen entities emerging simultaneously. Subsequently, two problems arise and restrict the performance of existing methods: (1) lack of the capability to model the long-dependency interactions between entities across different stages; (2) unable to exploit the incremental characteristics when KGs emerge in sequence. To address the problems effectively, we dive into the practical scenario formulated as Sequential-emerging Knowledge Graphs (SEKGs), and propose a novel model entitled ISE
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(Inductive Sequential Emerging Embedding). Specifically, ISE
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is composed of the following two modules: (1) a relational graph-transformer network is designed to capture long-dependency interactions with the full-graph receptive field; (2) an adaptive attention mechanism is developed to iteratively integrate emerging KGs into a whole, fully utilizing the incremental characteristic in SEKGs. Furthermore, a new benchmark that conforms to the data distribution of real-world sequential-emerging is constructed. The experimental results demonstrate the superiority of ISE
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compared with the state-of-the-art methods in SEKGs scenario.