Approximate Spreading Activation for Efficient Knowledge Retrieval from Large Datasets

被引：0

作者：

Grinberg, Maurice ^{[1
]}

Haltakov, Vladimir ^{[1
]}

Stefanov, Hristo ^{[1
]}

机构：

[1] New Bulgarian Univ, Cent & East European Ctr Cognit Sci, Sofia 1618, Bulgaria

来源：

NEURAL NETS WIRN10 | 2011年 / 226卷

关键词：

Spreading activation; semantic Web; large knowledge datasets; knowledge retrieval; CUDA;

D O I：

10.3233/978-1-60750-692-8-326

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes a new approximate implementation of Spreading Activation (SA) for knowledge selection in very large datasets. SA is used to prime relevant knowledge domains and reduce considerably the graph queried and therefore the query time. The method is based on the representation of the dataset as a sparse matrix of integers and the application on the corresponding graph of fast path searching algorithm which counts the number of times a node is reached following independent paths. The algorithm is implemented and tested on a CUDA enabled GPU on a dataset containing about 100 million of nodes and 850 million of statements. The numerical evaluation indicates that the approximate SA mechanism proposed is quite promising for real time applications achieving the activation of about 64 million nodes and 374 million of statements in about 5.5 seconds.

引用

页码：326 / 333

页数：8