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MRE: A translational knowledge graph completion model based on multiple relation embedding.

Xinyu Lu1, Lifang Wang1, Zejun Jiang1

  • 1School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Mathematical Biosciences and Engineering : MBE
|March 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Multiple Relation Embedding (MRE), a new model for knowledge graph completion (KGC). MRE effectively embeds multiple relation types, overcoming limitations of previous models and improving KGC performance.

Keywords:
entitiesknowledge graph completionknowledge graphsmultiple relationssemantic information

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Knowledge Representation and Reasoning

Background:

  • Knowledge graph completion (KGC) is crucial for leveraging knowledge graphs (KGs).
  • Existing KGC models often fail to capture the semantics of diverse relation types (direct, multi-hop, rule-based) and struggle with data sparsity.
  • Current translational and semantic matching models have limitations in handling multiple relation semantics and embedding sparse relations.

Purpose of the Study:

  • To propose a novel translational knowledge graph completion model, Multiple Relation Embedding (MRE).
  • To address the limitations of existing models by embedding multiple relation types for richer KG representation.
  • To improve KGC performance by capturing the semantics of direct, multi-hop, and rule-based relations simultaneously.

Main Methods:

  • Leveraged PTransE and AMIE+ to extract multi-hop and rule-based relations.
  • Developed two novel encoders to capture semantic information from multiple relations, including interactions between relations and entities.
  • Defined three energy functions based on the translational assumption and employed a joint training method for KGC.

Main Results:

  • The proposed Multiple Relation Embedding (MRE) model demonstrated superior performance compared to existing baselines in knowledge graph completion tasks.
  • Experimental results validated the effectiveness of embedding multiple relation types for enhancing KGC.
  • The novel encoders successfully captured complex semantic information and entity-relation interactions.

Conclusions:

  • Embedding multiple relation types is a promising approach to advance knowledge graph completion.
  • The MRE model effectively addresses limitations in handling diverse relation semantics and data sparsity.
  • The proposed method offers a significant improvement in KGC accuracy and KG representation.