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Path-Based Knowledge Graph Link Prediction Method With Graph Context
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DOI：10.1109/TCSS.2026.3669923.png)
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Abstract
En 中文
Knowledge graphs (KGs) are a crucial foundation for intelligent systems performing advanced tasks such as personalized recommendations and question answering. However, many existing KGs are incomplete because of missing triples. Recent studies have shown that link prediction based on paths can be a reliable approach. Path-based methods mainly use path patterns within the KG to solve the puzzle of missing triples. These patterns, defined by the paths connecting two entities, are used to predict the relation between them. However, these methods are not perfect; relying solely on the relation between entities or their embedding information is ineffective in link prediction. To address the above challenges, we propose a new KG link prediction method leveraging the path and graph context. For a given candidate relation and two entities, the type hierarchy of entities is used for disambiguation while preserving the entities’ neighborhood information in the graph. Our method then encodes the path information using a bi-long short-term memory (LSTM), integrates the path patterns with the attention mechanism, and finally predicts whether the entities can be linked via the candidate relations. Experiments on three standard datasets demonstrate the effectiveness of our method.
Keywords:
Graph context
knowledge graph (KG)
link prediction
path
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