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Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic

Nasrullah Khan1,2, Zongmin Ma1,3, Li Yan1

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China.

Applied Intelligence (Dordrecht, Netherlands)
|May 16, 2022
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Summary
This summary is machine-generated.

Knowledge Graph Embedding Enhancement (KGEE) using Hashing-based Semantic-relevance Attributed Graph-embedding Enhancement (H-SAGE) improves recommendation accuracy by reducing noise. This approach also tackles computational complexity for better performance.

Keywords:
DNNHashingInformation relevanceKGEEKnowledge graphRecommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Knowledge Graph Embedding (KGE) is vital for accurate recommendations.
  • Existing KGE methods lack information-relevance constraints, leading to noise and degraded performance.
  • High computational complexity is a significant challenge in KG-enhanced systems.

Purpose of the Study:

  • To propose a novel Knowledge Graph Embedding Enhancement (KGEE) approach named Hashing-based Semantic-relevance Attributed Graph-embedding Enhancement (H-SAGE).
  • To address noise penetration and computational time complexity in KGE.
  • To improve recommendation performance by modeling higher-order entities and relations effectively.

Main Methods:

  • Introduced Node Relevance-based Guided-walk (NRG) to model semantically-relevant entities and relations.
  • Developed Deep-Probabilistic (dProb) technique to convert information to hash-codes and manage hash-buckets.
  • Utilized Locality Sensitive (LS) hashing for efficient information retrieval during hash misses.

Main Results:

  • H-SAGE effectively models semantically-relevant higher-order entities and relations.
  • The dProb technique and LS hashing significantly reduce computational time complexity.
  • Experimental results on benchmark datasets show H-SAGE outperforms state-of-the-art methods in accuracy and computational efficiency.

Conclusions:

  • H-SAGE offers a robust solution to noise penetration and computational overhead in KGE.
  • The proposed NRG and dProb techniques enhance the precision and efficiency of graph embedding.
  • H-SAGE represents a significant advancement in KG-enhanced recommendation systems.