Access Structure Selection for Knowledge Graphs Based on Machine Learning

被引：0

作者：

Qi, Zhixin ^{[1
]}

Wang, Hongzhi ^{[1
]}

机构：

[1] Harbin Inst Technol, Harbin, Peoples R China

来源：

PROCEEDINGS OF THE ACM TURING AWARD CELEBRATION CONFERENCE-CHINA 2024, ACM-TURC 2024 | 2024年

基金：

中国国家自然科学基金;

关键词：

Knowledge Graph; Machine Learning; Performance Prediction; Storage Structure; Physical Design Tuning; Index Selection;

D O I：

10.1145/3674399.3674469

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, the rapid development of machine learning technology has provided opportunities for the automatic access structure selection of knowledge graph data. Considering that machine learning is suitable to describe the complex patterns and solve the complex optimization problems, this paper adopts machine learning techniques to predict the performance of knowledge graph storage structures, tune the storage structure of a knowledge graph, and select the index configurations for a knowledge graph automatically.

引用

页码：214 / 215

页数：2