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Multi-Task Learning and Improved TextRank for Knowledge Graph Completion.

Hao Tian1,2, Xiaoxiong Zhang2, Yuhan Wang3

  • 1School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.

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|July 8, 2023
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Summary
This summary is machine-generated.

This study introduces a new model for knowledge graph completion that effectively uses entity descriptions and relation features. The MIT-KGC model significantly improves accuracy in knowledge graph completion tasks.

Keywords:
a lite bidirectional encoder representations from transformers (ALBERT)extractive summarizationknowledge completionmulti-task learning

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Information Retrieval

Background:

  • Knowledge graph completion (KGC) is crucial for enhancing knowledge graphs and data quality.
  • Existing KGC methods often overlook relation features and struggle with lengthy, redundant entity descriptions.

Purpose of the Study:

  • To propose a novel multi-task learning and improved TextRank for knowledge graph completion (MIT-KGC) model.
  • To address limitations in current KGC methods by effectively utilizing entity descriptions and relation features.

Main Methods:

  • Key contexts extracted from entity descriptions using an improved TextRank algorithm.
  • A lite bidirectional encoder representations from transformers (ALBERT) model used as a text encoder to reduce parameters.
  • Multi-task learning to fine-tune the model by integrating entity and relation features.

Main Results:

  • Significant improvements in Mean Rank (MR), Hit@10, and Hit@3 on WN18RR, FB15k-237, and DBpedia50k datasets.
  • Enhanced performance compared to traditional KGC methods, demonstrating the model's validity.
  • Specific metric improvements include a 38% MR enhancement on WN18RR and a 1.5% Hit@1 improvement on DBpedia50k.

Conclusions:

  • The proposed MIT-KGC model effectively addresses challenges in knowledge graph completion.
  • The integration of improved TextRank and ALBERT with multi-task learning yields superior results.
  • The model demonstrates strong potential for improving knowledge graph quality and data supplementation.