Knowledge Graph Completion using Structural and Textual Embeddings

Sakher Khalil Alqaaidi, Krzysztof Kochut
4/24/2024
cs.AIcs.CL

No official code found. Best implementation: sa5r/KGRP (4 stars, MIT, last commit 2024-03-26).

Abstract

Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an opportunity to complete KGs by exploring relations between existing nodes, a task known as relation prediction. In this study, we propose a relations prediction model that harnesses both textual and structural information within KGs. Our approach integrates walks-based embeddings with language model embeddings to effectively represent nodes. We demonstrate that our model achieves competitive results in the relation prediction task when evaluated on a widely used dataset.

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Code Implementations(1)

sa5r/KGRPCommunity100%
MIT

Knowledge Graph Relation Prediction

40Shell, PythonMar 10, 20242 years agoMIT

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Cite this paper

@article{alqaaidi2024knowledge,
  title  = {Knowledge Graph Completion using Structural and Textual Embeddings},
  author = {Sakher Khalil Alqaaidi and Krzysztof Kochut},
  year   = {2024},
  eprint = {2404.16206},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.16206v1}
}

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