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Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs.

Hongbo Liu1, Jicang Lu1, Tianzhi Zhang1

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Summary

This study introduces SASILP, a novel inductive link prediction model for knowledge graphs. SASILP effectively incorporates relational semantics, outperforming existing methods in predicting unseen links.

Keywords:
Graph attention networkInductive link predicitonKnowledge graphsPersonalized PageRankRandom walk

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Graph Machine Learning

Background:

  • Inductive link prediction (ILP) in knowledge graphs (KGs) aims to predict missing links between entities unseen during training.
  • Existing subgraph-based methods often neglect relational semantics during subgraph extraction, limiting their effectiveness.

Purpose of the Study:

  • To introduce SASILP (Structure and Semantic Inductive Link Prediction), a novel model that integrates relational semantics into subgraph extraction and node initialization for ILP.
  • To enhance the accuracy of predicting missing links in knowledge graphs, particularly for unseen entities.

Main Methods:

  • SASILP employs a random walk strategy to compute structural scores of neighboring nodes.
  • An enhanced graph attention network determines semantic scores, which are integrated with structural scores.
  • Key nodes are selected to form a subgraph, initialized with a technique incorporating neighboring relation information.

Main Results:

  • Experiments on benchmark datasets show SASILP surpasses state-of-the-art methods in inductive link prediction.
  • The model's effectiveness in incorporating both structural and semantic information was verified.

Conclusions:

  • SASILP offers a significant advancement in inductive link prediction by effectively leveraging relational semantics.
  • The proposed approach demonstrates superior performance and validates the importance of integrating structure and semantics for unseen link prediction.