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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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FRS: A simple knowledge graph embedding model for entity prediction.

Li Fang Wang¹, Xin Yu Lu¹, Ze Jun Jiang¹

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

Mathematical Biosciences and Engineering : MBE

|November 9, 2019

Summary

This summary is machine-generated.

Feature Refactoring Scoring (FRS) enhances entity prediction by improving knowledge graph component interaction. This novel approach outperforms existing knowledge graph embedding methods on benchmark datasets.

Keywords:

FRS entity prediction knowledge graphs knowledge graphs embedding

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Area of Science:

Artificial Intelligence
Data Science
Machine Learning

Background:

Entity prediction is crucial for knowledge graph completion (KGC).
Traditional knowledge graph embedding (KGE) methods use simple operations, limiting component interaction and model performance.
Existing KGE models struggle with information incompleteness in knowledge graphs.

Purpose of the Study:

To propose a novel entity prediction model, Feature Refactoring Scoring (FRS).
To address insufficient interaction and information incompleteness in knowledge graphs.
To improve the performance of entity prediction models.

Main Methods:

FRS employs a feature processing procedure for alignment in the same feature space.
Entities and relationships are processed to enhance interaction.
A simple three-layer network architecture is utilized.

Main Results:

FRS outperforms state-of-the-art KGC methods on FB15K and WN18 datasets.
The model demonstrates improved performance compared to traditional KGE methods.
Extensive experiments revealed insights into embedding size and negative sampling probability.

Conclusions:

FRS effectively alleviates insufficient interaction and information incompleteness in KGs.
The feature refactoring approach enhances entity prediction accuracy.
FRS offers a promising alternative to conventional KGE techniques.